Vibration　signal　analysis　is　one　of　the　most　effective　methods　for　mechanical　fault　diagnosis.　Available　part　of　the　information　is　always　concealed　in　component　noise,　which　makes　it　much　more　difficult　to　detect　the　defection,　especially　at　early　stage　of　the　development.　This　paper　presents　a　new　approach　for　mechanical　fault　diagnosis　based　on　time　domain　analysis　and　adaptive　fuzzy　C-means　clustering.　By　analyzing　vibration　signal　collected,　nine　common　time　domain　parameters　are　calculated.　This　lot　of　data　constitutes　data　matrix　as　characteristic　vectors　to　be　detected.　And　using　adaptive　fuzzy　C-means　clustering,　the　optimal　clustering　number　can　be　gotten　then　to　recognize　different　fault　types.　Moreover,　five　parameters,　including　variance,　RMS,　kurtosis,　skewness,　and　crest　factor,　of　the　nine　are　selected　as　the　new　eigenvector　matrix　to　be　clustered　for　more　optimal　clustering　performance.　The　test　results　demonstrate　that　the　proposed　approach　has　a　sensitive　reflection　towards　fault　identifications,　including　slight　fault.