As manufacturing quality has become a decisive factor in global market competition, quality control techniques such as Statistical Process Control (SPC) are becoming popular in industries. With advances in information, sensing, and data capture technology, large volumes of data are being routinely collected and shared over multiple-stage processes, which have growing impacts on the existing quality control methods that usually focus only on individual stage in the process. This research tackles some unique issues due to multistage interaction, and proposes an enhanced cause-selecting chart to monitor and diagnose a multistage process.