This article describes the ideas and methods that led to the writing of an algorithmic composition NTrope Suite. This piece, for solo recorders and voice, was generated by ‘mixing' works by different composers from different eras. The idea behind the work was to examine random generation procedures that could maintain stylistic properties typical to the reference works. The interesting property of this method is that it implements a sort of ‘statistical learning', that optimally preserves the properties of the reference pieces and also properly ‘generalises' them so as to create a new valid work. The musical result is very coherent, maintaining both stylistic resemblance to the reference music and exhibiting some surprising originality as well. Theoretically, the resulting piece is closest to the reference works in terms of mutual entropy. The algorithm and its theoretical significance are discussed.