Abstract: Based on the in-situ data logging in a steel works in China, a linear model was developed to calculate the finishing temperature during hot strip rolling by way of stepwise regression analysis. The main influencing factors on finishing temperature were taken into account in modeling, such as rolling speed, workpiece thickness at entry and exit of finish rolling and inter-stand cooling water flow rate. A simulative computation was therefore done according to the linear model before starting the self-learning of model regression coefficients, then the computational results were compared with actual data logging. It was found that 70.1% of the difference between them is less than 15°C, which implies that the computational results of the model can meet the precision control requirement for finishing temperature. Furthermore, because of the limitation of the linear regression model, the self-learning of regression coefficients of the model was discussed.