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A neural network architecture is applied to the problem of direction of arrival (DOA) estimation using a linear array. A three layer radial basis function network (RBFN) is trained with input output pairs. The network is then capable of estimating DOA not included in the training set through generalization. This approach reduces the extensive computations required by conventional superresolution algorithms such as MUSIC and is easier to implement in real-time. The results suggest that the performance of the RBFNN method approaches that of the MUSIC algorithm. In real time the fast convergence rates of neural networks will allow the array to track mobile sources.