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In this paper, we propose a music recommendation system based on user preference analysis. The system builds music models using hidden Markov models with mel frequency cepstral coefficients, which are features of sound wave. Each song is modeled with an HMM and the similarity measure between songs are defined based on the models. With the similarity measure, the songs the user listened to in the past are grouped and analyzed. The system recommends pieces of music to the user based on the result of the analysis. We evaluate our system with virtual users who have various preferences, and observe which recommendation lists the system generates. In most cases, the system recommends the pieces of music which are close to userpsilas preference.