The motive for this study is to improve learning based target recognition by utilizing discriminative learning for minimum error classification and applying discriminative feature extraction based on a preset criterion. Radar cross section measurements of four commercial aircraft, obtained experimentally in a compact range, are used for training and testing a three layered back propagation neural network for target identification purposes. It is assumed that the aspect angles (or azimuth positions) of all four targets are either completely known or known within 20 degrees uncertainty range. The scattering parameters of each target are assigned selective weights and presented to a discriminative feature extractor. The performance of the proposed target recognition system is examined assuming different noise scenarios and various levels of azimuth ambiguity. The proposed scheme is also tested in scenarios where the maximum likelihood approach is available and the performances of both recognition techniques are compared. Issues concerning the number of hidden nodes, training parameters, and weight convergence are discussed.