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A neural network architecture is applied to the problem of direction of arrival (DOA) and state of polarization estimation using a uniform circular cross and tri-crossed-dipoles antenna 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 and the corresponding state of polarization. This approach reduces the extensive computations required by conventional super resolution algorithms such as MUSIC and is easier to implement in real-time applications. The results suggest that the performance of the RBFNN method approaches the exact values. In real time, fast convergence rates of neural networks will allow the array to track mobile sources.