Control charts are a basic means for monitoring the quality characteristics of a manufacturing process to ensure the required quality level. They are used to track product and process variations through graphical representation of the quality variable of interest. A control chart shows the state of control of a process and can exhibit different types of patterns which are indicative of long-term trends in it. This paper describes the integration of an expert system and a neural-network-based pattern recognizer for analysing and interpreting control charts. The expert system has an on-line process monitoring package to detect general out-of-control situations and a diagnosis module to suggest corrective actions. The pattern recognizer is an on-line system comprising two neural networks and an heuristics module designed to identify incipient process abnormalities from control chart patterns. The paper also compares neural networks and expert systems and provides the rationale for the integration process.