Statistical process control (SPC) is a useful technique for process monitoring that draws heavily on control charts as its important tools. Designing effective control charts, in turn, requires their relevant parameters to be determined through proper cost functions. In nearly all the methods used for this purpose, a major drawback is the negligence of type II errors, which causes the process to function out of control, and thereby imposing too high, or at times irrecoverable, costs on the system. One way to decrease this error and the associated out-of-control times and costs is to combine control charts with maintenance management (MM) programs. In this paper, a hybrid model is used to combine the control chart and a planned maintenance (PM) system for rapid detection of out-of-control states and reduction of the costs associated with system control. Accordingly, the multivariate exponentially weighted moving average (MEWMA) control chart is used for controlling the process variability, and the maintenance plan can stop the process at specific times and check whether the process is in- or out of control. In addition, if the control chart signals an out-of-control state before the scheduled maintenance time, a reactive maintenance (RM) is implemented in order to restore the process to its in-control state. A numerical illustration is finally used, and comparisons are made to show the capability of the proposed hybrid model in yielding average reductions of 6.5 and 4.3 %, respectively, in the costs of monitoring-maintenance (MM) system compared to situations in which either the control chart method or the maintenance model is used alone.