In a control chart, unnatural patterns are always associated with some specific assignable causes that should be eliminated. The identification of control chart pattern (CCP) is therefore important and further estimation of the unnatural pattern parameters can improve the manufacturing process. In this paper, a modified counter-propagation network (m-CPN) is developed to classify the mean shift and simultaneously estimate the shift magnitude. The m-CPN is compared with five existing networks through numerical simulation and the result shows a better performance of the m-CPN in terms of classification accuracy, as well as both Type I and Type II errors.