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This paper describes a system able to identify a music work through the analysis of the audio recording of a per- formance. The approach is based on the statistical model- ing of the expected audio features of music performances, given a database of known music works. In particular, the automatic identification is based on an application of hid- den Markov models (HHMs), which are automatically built from music scores available in digital format. States of the HMMs are labeled by score events, and transition and ob- servation probabilities are directly computed from the in- formation on the score. Three alternative approaches to the identification task have been proposed and tested on a set of audio excerpts. Results showed that the methodology can achieve satisfactory results. A prototype system has been developed, and will be demonstrated, which allows in a few seconds to identify an unknown recording from a dataset of hundreds of scores.