A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analyze the performance of applied neural network the comparison with the measured rolling force and the predicted results using two different neural networks-RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.