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Location estimation of mobile users has been required in cellular networks for E-911 callers as a mandatory service. In this paper, radial basis function (RBF) neural networks with a hierarchical structure are proposed to estimate the mobile user location. At each level of hierarchical structure, the coverage area of base station is divided to different cells whose areas have overlaps. The RBF network of each cell is trained based on the received signals by an antenna array from the coverage area of that cell. The trained network estimates the mobile user location based on the power and arrival direction of the received signal. Mobile location estimation is improved by zooming in the smaller cell at each level of the hierarchical structure. Simulation results show that the proposed estimation method achieves a good performance with robustness in non-line of sight propagation and urban environments.