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An artificial neural network is applied to the estimation of strength of acid sites synergistically generated in binary mixed oxides. The acid strength is represented as a function of physical/chemical properties of both constituent oxides. The form of the function is represented by a back-propagation neural network which was trained by using the published acid strength data for such cases. The estimated results were in a good agreement with the experimentally observed ones.