The variation of the friction in the roll bite is of great importance in cold strip rolling. The main interest of the paper is to model the friction coefficient in the roll bite during cold rolling. The deformation resistance of the rolled products and friction coefficient in the roll bite were determined simultaneously by minimizing the error of the measured and calculated rolling forces based on nonlinear least squares optimization algorithm. The neural network was introduced to further improve the accuracy of friction coefficient calculation in cold strip rolling. The results already obtained shows that friction decreases with roll wear, and the lower the rolling speed, the higher is the friction.