Abstract. The main area of work in computer music related to information systems is known as music information retrieval (MIR). Databases containing musical information can be classified into two main groups: those containing audio data (digitized music) and those that file symta (digital music scores). The latter are much more abstract that the former ones and contain a lot of information already coded in terms of musical symbols, thus MIR algorithms are easier and more efficient when dealing with symbolic databases. The automatic extraction of the notes in a digital musical signal (automatic music transcription) permits applying symbolic processing algorithms to audio data. In this work we analize the performance of a neural approach and a well known non parametric algorithm, like nearest neighbours, when dealing with this problem using spectral pattern identification.