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The performance of photovoltaic power system interconnected with utility grid can be improved through an application of advanced control method. This paper introduces an application of an artificial neural network on the operation control of the photovoltaic/utility grid to improve system efficiency and reliability. The generated power from photovoltaic solar cells array have been calculated by a computer program under known insolation and temperature. The computer program which proposed here and applied to carry out these calculations is based on the minimization of the power purchase from grid. This paper focus on a hybrid system consists of photovoltaic system accompanied with battery storage interconnected with utility grid taking into account the variation of solar radiation and load demand during the day. Different feed forward neural network architectures are trained and tested with data containing a variety of operation patterns. A simulation is carried out over one year using the hourly data of the load demand, insolation and temperature at El'Zafranna site, Egypt as a case study. The results show that the selected neural network architecture (5+9+4) gives reasonably accurate operation of photovoltaic/utility grid accompanied with battery storage.