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A hybrid classifier obtained by hybridizing Support Vector Machines (SVM) and Artificial Neural Network (ANN) classifiers is presented here for diagnosis of gear faults. The distinctive features obtained from vibration signals of a running gearbox, which was operated in normal and fault-induced conditions, were used to feed the SVM-ANN hybrid classifier. Time-domain vibration signals were divided in segments. Features such as peaks in time domain and in spectrum, central moments, and standard deviations were obtained from signal segments. Based on the experimental results, it was shown that SVM-ANN hybrid classifier can successfully identify gear condition and that the hybrid SVM-ANN classifier performs much better than standard versions of ANNs and SVM. The effectiveness of the hybrid classifier under noise was also investigated. It was shown that if vibration signals are preprocessed by Discrete Wavelet Transform (DWT), efficacy of the SVM-ANN hybrid is significantly enhanced.