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The ever-increasing density of computer storage devices has allowed the average user to store enormous quantities of multimedia content, and a large amount of this content is usually music. Current search techniques for musical content rely on meta-data tags which describe artist, album, year, genre, etc. Query-by-content systems allow users to search based upon the acoustical content of the songs. Recent systems have mainly depended upon textual representations of the queries and targets in order to apply common string-matching algorithms. However, these methods lose much of the information content of the song and limit the ways in which a user may search. We have created a music recommendation system that uses self-organizing maps to find similarities between songs while preserving more of the original acoustical content. We build on the design of the recommendation system to create a musical query-by-content system. We discuss the weaknesses of the naive solution and then implement a quasi-supervised design and discuss some preliminary results.