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Signal processing with neural networks has become popular. The inherent merits of neural networks make it attractive in many applications. We propose the use of cross-correlation neural network models which makes use of the cyclo-stationary property inherent in many communication signals to perform blind beamforming. The proposed approach is based on two sets of linear neurons with cross-coupled Hebbian learning rules orthogonalized to each other. Taking the array data and its time-frequency translated version as inputs, the neural network extracts and separates the desired signals simultaneously. This approach may have advantages in multi-user wireless communications where the co-channel interference condition is severe or the number of interferences is larger than the number of array elements.