Abstract: To avoid local minimum solutions in the back propagation learning, a new global optimization algorithm based on filled function is proposed to model the product quality of continuous casting furnace and hot rolling mill. The industrial process is very complicated and the E umber of parameters which determine the final properties can be quite large. It is extremely difficult to develop a physical model for predicting various properties like elongation and yield and tensile strengths. In the present work, a wavelet neural network has been employed to develop a quantitative method for estimating the elongation and yield and tensile strengths as a function of steel composition and rolling parameters. Experimental studies demonstrate that the predicted mechanical properties have a good agreement with the measured data by using the developed wavelet network model.