A new approach was proposed by combing Ensemble Empirical Mode Decomposition (EEMD) algorithm and Back Propagation (BP) neural network for detection of gear through transmission noise analysis. Then feature values of the feature signals are calculated. The feature values which have a great difference for different defect types are chosen to build an eigenvector. BP neural network is used to train and learn on the eigenvector for recognition of gear defects intelligently. In this study, a comparative experiment has been performed among normal gears, cracked gears and eccentric gears with fifteen sets of different gears. Experimental results indicate that the proposed method can detect gear defect features carried by the transmission noise effectively.