We consider the problem of measuring the similarity of streaming music content and present a method for modeling, on the fly, the temporal progression of a song’s timbre. Using a minimum distance classification scheme, we give an approach to classifying streaming music sources and present performance results for auto-associative song identification and for content-based clustering of streaming music. We discuss possible extensions to the approach and possible uses for such a system.