The　condition　monitoring　and　fault　diagnosis　of　rolling　element　bearings　are　particularly　crucial　in　rotating　mechanical　applications　in　industry.　A　bearing　fault　signal　contains　information　not　only　about　fault　condition　and　fault　type　but　also　the　severity　of　the　fault.　This　means　fault　severity　quantitative　analysis　is　one　of　most　active　and　valid　ways　to　realize　proper　maintenance　decision.　Aiming　at　the　deficiency　of　the　research　in　bearing　single　point　pitting　fault　quantitative　diagnosis,　a　new　back-propagation　neural　network　method　based　on　wavelet　packet　decomposition　coefficient　entropy　is　proposed.　The　three　levels　of　wavelet　packet　coefficient　entropy(WPCE)　is　introduced　as　a　characteristic　input　vector　to　the　BPNN.　Compared　with　the　wavelet　packet　decomposition　energy　ratio　input　vector,　WPCE　shows　more　sensitive　in　distinguishing　from　the　different　fault　severity　degree　of　the　measured　signal.　The　engineering　application　results　show　that　the　quantitative　trend　fault　diagnosis　is　realized　in　the　different　fault　degree　of　the　single　point　bearing　pitting　fault.　The　breakthrough　attempt　from　quantitative　to　qualitative　on　the　pattern　recognition　of　rolling　element　bearings　fault　diagnosis　is　realized.