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The multilayered feed-forward neural network was applied to automatic target recognition using the high range resolution (HRR) profiles in this paper. To extract effective features from the HRR profiles, the product spectrum originally proposed for the speech signal processing was introduced to the radar target recognition community. The product spectrum was defined as the product of the power spectrum and the group delay function, which could combine the information contained in the magnitude spectrum and phase spectrum of the HRR profiles and carry more details about the shape of the aircrafts. A multilayered feed-forward neural network was selected as classifier. The HRR profiles were obtained using the two-dimensional backscatters distribution data of four different scaled aircraft models. Simulations were presented to evaluate the classification performance with the product spectrum based features. The results demonstrate that the product spectrum based features outperform the original HRR profiles and the multilayered feed-forward neural network is effective for the application of automatic target recognition of aircrafts.