In　order　to　automatically　recognize　different　scales　of　bearing　faults,　a　method　of　pattern　recognition　based　on　the　multiwavelet　packet　sample　entropy　method　and　BP　neural　network　is　put　forth.　First,　the　vibration　signals　of　rolling　bearings　with　five　different　scaled　outer　race　defects　are　decomposed　into　three　layers　using　the　GHM　multiwavelet　packet.　The　signal　sample　entropy　of　16　decomposed　frequency　bands　are　then　used　as　the　neural　network＇s　input　vector,　so　that　the　complete　information　of　the　multiwavelet　packet　decomposition　can　be　thoroughly　utilized.　Based　on　the　learning　and　training　of　a　three-layer　BP　neural　network,　and　in　comparison　with　the　dB10　wavelet　packet,　it　can　be　concluded　that　the　convergence　speed　and　identification　accuracy　of　the　multiwavelet　packet　sample　entropy　method　is　much　better　than　those　of　the　traditional　wavelet　neural　network　classification.　As　a　result,　the　multiwavelet　packet　sample　entropy　method　is　effective　in　automatically　recognizing　different　scales　of　bearing　faults.　©,　2015,　Nanjing　University　of　Aeronautics　an　Astronautics.　All　right　reserved.