An adaptive neural network learning algorithm is used to estimate the location of radar targets scattering centers. The performance of the implemented spectral estimation scheme depends on learning rate, number of training vectors, and the length of each training vector. The neural network learning spectral estimation algorithms used in this study is an attractive alternative to traditional high resolution spectral estimation schemes because the number of spectral peaks depends not only on a model order assigned a priori, but also on the level of training, learning rate, and convergence. Also, neural networks are adaptive to changes in data, fault tolerant, and may be implemented using analog circuitry. Scattering features extracted using neural networks are used for target classification. The performance of the proposed target recognition scheme is compared with that of nearest neighbor based classifier.