Injection molding is characterized by complex dynamics, which makes quality difficult to control. This is because the exact relations among the machine inputs, material properties, and molded part quality are not known precisely. Hence, the existing models for quality prediction have a limited accuracy and difficulty in application to general molding applications. This article investigates the integration of analytical process knowledge and artificial neural networks as a solution for quality prediction of molded parts, with accuracy increased toward quality control targets of three defects per million (60). This article describes the hybrid system based on the neural network and process knowledge, then compares its performances with conventional neural models for the prediction of the injection pressure.