The motive for this study is to achieve near aspect-independent target identification with the assumption that radar signatures have fuzzy memberships in more than one class. 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 known within 20 degrees uncertainty range. The radar cross section parameters of each target are assigned to three fuzzy membership functions, and for each combination of membership functions there is a desired fuzzy output. The performance of the proposed target recognition system is examined assuming different noise scenarios and various levels of azimuth ambiguity. The proposed fuzzy neural scheme is also tested in scenarios where the maximum likelihood is available and the performances of both recognition techniques are compared. It is assumed that radar target signatures have fuzzy class separation. Issues concerning the number of hidden nodes, training parameters, and weight convergence are discussed.