Noise　signal　analysis　method　is　widely　available　for　gearbox　bevel　gear　fault　detection.　However,　the　noise　from　the　gearbox　is　usually　concealed　by　background　noise,　which　leads　to　poor　efficiency　analysis.　This　paper　reports　an　ensemble　empirical　mode　decomposition　(EEMD)　and　neural　network　method　for　bevel　gear　fault　detection.　To　extract　useful　signal,　EEMD　algorithm　was　firstly　applied　to　get　rid　of　the　background　noise.　Characteristics　from　a　group　of　discriminating　defect　status　were　then　chosen　to　build　the　eigenvector.　Finally,　the　eigenvector　was　imported　into　a　back　propagation　(BP)　neural　network　classifier　for　defect　diagnosis　automatically.　Experimental　results　show　that　the　proposed　approach　is　capable　for　signal　denoising　and　providing　distinguishing　characteristics　of　founded　fault.　The　developed　method　is　an　accurate　approach　to　detect　fault　for　tested　bevel　gear.