diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1360528..a9a38d2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # Changelog
 
+## bliss 0.3.5
+* Add custom sorting methods for playlist-making.
+
 ## bliss 0.3.4
 * Bump ffmpeg's version to avoid building ffmpeg when building bliss.
 
diff --git a/Cargo.toml b/Cargo.toml
index 912e1c8..1c89caa 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "bliss-audio"
-version = "0.3.4"
+version = "0.3.5"
 authors = ["Polochon-street <polochonstreet@gmx.fr>"]
 edition = "2018"
 license = "GPL-3.0-only"
diff --git a/src/distance.rs b/src/distance.rs
index bd13e09..0db1cd3 100644
--- a/src/distance.rs
+++ b/src/distance.rs
@@ -7,10 +7,12 @@
 //! They will yield different styles of playlists, so don't hesitate to
 //! experiment with them if the default (euclidean distance for now) doesn't
 //! suit you.
-use crate::NUMBER_FEATURES;
 #[cfg(doc)]
-use crate::{Library, Song};
+use crate::Library;
+use crate::Song;
+use crate::NUMBER_FEATURES;
 use ndarray::{Array, Array1};
+use noisy_float::prelude::*;
 
 /// Convenience trait for user-defined distance metrics.
 pub trait DistanceMetric: Fn(&Array1<f32>, &Array1<f32>) -> f32 {}
@@ -36,10 +38,158 @@ pub fn cosine_distance(a: &Array1<f32>, b: &Array1<f32>) -> f32 {
     1. - similarity
 }
 
+/// Sort `songs` in place by putting songs close to `first_song` first
+/// using the `distance` metric. Deduplicate identical songs.
+pub fn closest_to_first_song(
+    first_song: &Song,
+    songs: &mut Vec<Song>,
+    distance: impl DistanceMetric,
+) {
+    songs.sort_by_cached_key(|song| n32(first_song.custom_distance(song, &distance)));
+    songs.dedup_by_key(|song| n32(first_song.custom_distance(song, &distance)));
+}
+
+/// Sort `songs` in place using the `distance` metric and ordering by
+/// the smallest distance between each song. Deduplicate identical songs.
+///
+/// If the generated playlist is `[song1, song2, song3, song4]`, it means
+/// song2 is closest to song1, song3 is closest to song2, and song4 is closest
+/// to song3.
+pub fn song_to_song(first_song: &Song, songs: &mut Vec<Song>, distance: impl DistanceMetric) {
+    let mut new_songs = vec![first_song.to_owned()];
+    let mut song = first_song.to_owned();
+    loop {
+        if songs.is_empty() {
+            break;
+        }
+        songs 
+            .retain(|s| n32(song.custom_distance(s, &distance)) != 0.);
+        songs.sort_by_key(|s| n32(song.custom_distance(s, &distance)));
+        song = songs.remove(0);
+        new_songs.push(song.to_owned());
+    }
+    *songs = new_songs;
+}
+
 #[cfg(test)]
 mod test {
     use super::*;
+    use crate::Analysis;
     use ndarray::arr1;
+    use std::path::Path;
+
+    #[test]
+    fn test_song_to_song() {
+        let first_song = Song {
+            path: Path::new("path-to-first").to_path_buf(),
+            analysis: Analysis::new([
+                1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let first_song_dupe = Song {
+            path: Path::new("path-to-dupe").to_path_buf(),
+            analysis: Analysis::new([
+                1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+
+        let second_song = Song {
+            path: Path::new("path-to-second").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1.9, 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let third_song = Song {
+            path: Path::new("path-to-third").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.5, 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let fourth_song = Song {
+            path: Path::new("path-to-fourth").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let mut songs = vec![
+            first_song.to_owned(),
+            first_song_dupe.to_owned(),
+            second_song.to_owned(),
+            third_song.to_owned(),
+            fourth_song.to_owned(),
+        ];
+        song_to_song(&first_song, &mut songs, euclidean_distance);
+        assert_eq!(
+            songs,
+            vec![first_song, second_song, third_song, fourth_song],
+        );
+    }
+
+    #[test]
+    fn test_sort_closest_to_first_song() {
+        let first_song = Song {
+            path: Path::new("path-to-first").to_path_buf(),
+            analysis: Analysis::new([
+                1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let first_song_dupe = Song {
+            path: Path::new("path-to-dupe").to_path_buf(),
+            analysis: Analysis::new([
+                1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+
+        let second_song = Song {
+            path: Path::new("path-to-second").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1.9, 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let third_song = Song {
+            path: Path::new("path-to-third").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.5, 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let fourth_song = Song {
+            path: Path::new("path-to-fourth").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+        let fifth_song = Song {
+            path: Path::new("path-to-fifth").to_path_buf(),
+            analysis: Analysis::new([
+                2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
+            ]),
+            ..Default::default()
+        };
+
+        let mut songs = vec![
+            first_song.to_owned(),
+            first_song_dupe.to_owned(),
+            second_song.to_owned(),
+            third_song.to_owned(),
+            fourth_song.to_owned(),
+            fifth_song.to_owned(),
+        ];
+        closest_to_first_song(&first_song, &mut songs, euclidean_distance);
+        assert_eq!(
+            songs,
+            vec![first_song, second_song, fourth_song, third_song],
+        );
+    }
 
     #[test]
     fn test_euclidean_distance() {
diff --git a/src/library.rs b/src/library.rs
index e2f8858..c26bc42 100644
--- a/src/library.rs
+++ b/src/library.rs
@@ -5,10 +5,9 @@
 //! MPD](https://github.com/Polochon-street/blissify-rs) could also be useful.
 #[cfg(doc)]
 use crate::distance;
-use crate::distance::DistanceMetric;
+use crate::distance::{closest_to_first_song, DistanceMetric, euclidean_distance};
 use crate::{BlissError, BlissResult, Song};
 use log::{debug, error, info};
-use noisy_float::prelude::*;
 use std::sync::mpsc;
 use std::sync::mpsc::{Receiver, Sender};
 use std::thread;
@@ -30,7 +29,8 @@ pub trait Library {
     /// once.
     fn get_stored_songs(&self) -> BlissResult<Vec<Song>>;
 
-    /// Return a list of songs that are similar to ``first_song``.
+    /// Return a list of `playlist_length` songs that are similar
+    /// to ``first_song``, deduplicating identical songs.
     ///
     /// # Arguments
     ///
@@ -40,21 +40,22 @@ pub trait Library {
     ///
     /// # Returns
     ///
-    /// A vector of `playlist_length` Songs, including `first_song`, that you
+    /// A vector of `playlist_length` songs, including `first_song`, that you
     /// most likely want to plug in your audio player by using something like
     /// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
+    // TODO return an iterator and not a Vec
     fn playlist_from_song(
         &self,
         first_song: Song,
         playlist_length: usize,
     ) -> BlissResult<Vec<Song>> {
-        let mut songs = self.get_stored_songs()?;
-        songs.sort_by_cached_key(|song| n32(first_song.distance(&song)));
+        let playlist = self.playlist_from_song_custom(
+            first_song,
+            playlist_length,
+            euclidean_distance,
+            closest_to_first_song,
+        )?;
 
-        let playlist = songs
-            .into_iter()
-            .take(playlist_length)
-            .collect::<Vec<Song>>();
         debug!(
             "Playlist created: {}",
             playlist
@@ -67,7 +68,7 @@ pub trait Library {
     }
 
     /// Return a list of songs that are similar to ``first_song``, using a
-    /// custom distance metric.
+    /// custom distance metric and deduplicating indentical songs.
     ///
     /// # Arguments
     ///
@@ -98,13 +99,13 @@ pub trait Library {
         playlist_length: usize,
         distance: impl DistanceMetric,
     ) -> BlissResult<Vec<Song>> {
-        let mut songs = self.get_stored_songs()?;
-        songs.sort_by_cached_key(|song| n32(first_song.custom_distance(&song, &distance)));
+        let playlist = self.playlist_from_song_custom(
+            first_song,
+            playlist_length,
+            distance,
+            closest_to_first_song,
+        )?;
 
-        let playlist = songs
-            .into_iter()
-            .take(playlist_length)
-            .collect::<Vec<Song>>();
         debug!(
             "Playlist created: {}",
             playlist
@@ -116,6 +117,44 @@ pub trait Library {
         Ok(playlist)
     }
 
+    /// Return a playlist of songs, starting with `first_song`, sorted using
+    /// the custom `sort` function, and the custom `distance` metric.
+    ///
+    /// # Arguments
+    ///
+    /// * `first_song` - The song the playlist will be built from.
+    /// * `playlist_length` - The playlist length. If there are not enough
+    ///    songs in the library, it will be truncated to the size of the library.
+    /// * `distance` - a user-supplied valid distance metric, either taken
+    ///    from the [distance](distance) module, or made from scratch.
+    /// * `sort` - a user-supplied sorting function that uses the `distance`
+    ///    metric, either taken from the [distance](module), or made from
+    ///    scratch.
+    ///
+    /// # Returns
+    ///
+    /// A vector of `playlist_length` Songs, including `first_song`, that you
+    /// most likely want to plug in your audio player by using something like
+    /// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
+    fn playlist_from_song_custom<F, G>(
+        &self,
+        first_song: Song,
+        playlist_length: usize,
+        distance: G,
+        mut sort: F,
+    ) -> BlissResult<Vec<Song>>
+    where
+        F: FnMut(&Song, &mut Vec<Song>, G),
+        G: DistanceMetric,
+    {
+        let mut songs = self.get_stored_songs()?;
+        sort(&first_song, &mut songs, distance);
+        Ok(songs
+            .into_iter()
+            .take(playlist_length)
+            .collect::<Vec<Song>>())
+    }
+
     /// Analyze and store songs in `paths`, using `store_song` and
     /// `store_error_song` implementations.
     ///
@@ -595,4 +634,49 @@ mod test {
                 .unwrap()
         );
     }
+
+    fn custom_sort(_: &Song, songs: &mut Vec<Song>, _: impl DistanceMetric) {
+        songs.sort_by_key(|song| song.path.to_owned());
+    }
+
+    #[test]
+    fn test_playlist_from_song_custom() {
+        let mut test_library = TestLibrary::default();
+        let first_song = Song {
+            path: Path::new("path-to-first").to_path_buf(),
+            analysis: Analysis::new([0.; 20]),
+            ..Default::default()
+        };
+
+        let second_song = Song {
+            path: Path::new("path-to-second").to_path_buf(),
+            analysis: Analysis::new([0.1; 20]),
+            ..Default::default()
+        };
+
+        let third_song = Song {
+            path: Path::new("path-to-third").to_path_buf(),
+            analysis: Analysis::new([10.; 20]),
+            ..Default::default()
+        };
+
+        let fourth_song = Song {
+            path: Path::new("path-to-fourth").to_path_buf(),
+            analysis: Analysis::new([20.; 20]),
+            ..Default::default()
+        };
+
+        test_library.internal_storage = vec![
+            first_song.to_owned(),
+            fourth_song.to_owned(),
+            third_song.to_owned(),
+            second_song.to_owned(),
+        ];
+        assert_eq!(
+            vec![first_song.to_owned(), fourth_song, second_song],
+            test_library
+                .playlist_from_song_custom(first_song, 3, custom_distance, custom_sort)
+                .unwrap()
+        );
+    }
 }