In　this　paper,　the　projective　non-negative　matrix　factorization　(PNMF)　with　Bregman　divergence　is　applied　into　the　musical　instrument　classification.　A　novel　supervised　learning　algorithm　for　automatic　classification　of　individual　musical　instrument　sounds　is　addressed　inspiring　from　PNMF　with　several　versions　of　Bregman　divergence.　Moreover,　the　orthogonality　of　basis　matrices　between　PNMF　and　conventional　non-negative　matrix　factorization　(NMF)　is　compared.　In　addition,　three　classifiers　based　on　nearest　neighbors　(NN),　Gaussian　mixture　model　(GMM)　and　radial　basis　function　(RBF)　are　added　to　evaluate　the　performance　of　PNMF　classifier.　The　results　indicate　that　the　classification　accuracy　of　the　proposed　PNMF　classifier　outperforms　the　classifiers　derived　from　conventional　NMF　and　machine　learning.　©　2012　IEEE.