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A fuzzy neural network (FNN) based inter-turn short circuit fault detection scheme for generator is proposed. The second harmonic magnitude of field current and the negative sequence components of voltages and currents are used as inputs for the FNN fault detector. The negative sequence voltage and current are obtained from the phase voltages and currents using the symmetrical component analysis method. And the second harmonic magnitude of field current is achieved by the FFT technique. The FNN fault detector with Gauss membership functions is trained off-line using the training data which comes from the Multi-Loop simulation program. The proposed fault detection scheme can perform the inter-turn short circuit fault detection, the fault type classification, and the fault location identification. Experimental results corroborate the effectiveness of the proposed scheme, which is implemented on a TI's DSP.