In　view　of　the　non-linear　and　non-stationary　of　the　rolling　element　bearing　fault　signal,　the　method　of　mathematical　morphology　analysis　is　introduced　into　the　rolling　element　bearing　fault　diagnosis.　Multi-scale　morphological　transform　is　applied　to　the　analysis　of　the　bearing　signals.　To　describe　the　complexity　of　pattern　spectrum　curves　by　using　sample　entropy,　and　its　value　as　the　input　vector　of　the　neural　network　is　used　to　realize　the　fault　pattern　classification　by　using　the　back-propagation　(BP)　neural　network.　Experimental　results　show　that　this　method　is　effective.