The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision-making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The key issues studied in this paper include optimal feature extraction using descriptive analysis, optimal feature subset selection using statistical hypothesis testing, machine fault prediction using multivariate process control chart, and diagnostic performance assessment using process trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production. An example of the analysis at one Ford production plant is provided to demonstrate the implementation of this methodology.