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This paper focuses on the close relationship between statistical process control and preventive maintenance (PM) of manufacturing equipment. The context is very general: a production process that is characterized by multiple distinct operational states and a failure state. The operational states differ in terms of operational/quality costs and/or the proneness to complete failure. The times of shift from the normal operational state to an inferior one and the times to failure are random variables, not necessarily exponentially distributed. The process is monitored with a control chart with the purpose of quickly detecting shifts to an inferior operational state due to the occurrence of some unobservable assignable cause. At the same time, the information collected from the process may be used to re‐schedule the planned PM, if there is evidence that a failure is imminent. The two mechanisms are obviously related, especially if they are based on measurements of the same critical process characteristic. Yet, they are typically treated independently. We develop a fairly general mathematical model for the joint optimization of the control chart parameters and the maintenance times. Numerical investigation using this model shows that ignoring the close relationship between process control and maintenance results in inefficiencies that may be substantial. It also provides practical insights about the effects of some key problem characteristics on the optimal joint design of process control and maintenance.