Abstract: In order to improve process output quality, a method for process quality prediction and multi-factor integrated optimizing is proposed. This method combines self-organized map neural network (SOMNN), principal component analysis (PCA) and radial basis function neural network (RBFNN). SOMNN is used to classify the process data. PCA is used to evaluate the classified data and establish the process factors repository. RBFNN is used to establish the process prediction model, determine the conformity of the process output quality by predicting, and propose the scheme for multi-factor integrated optimizing. The case analysis result shows that the method is effective and can achieve preventive quality improvement.