This article proposes a methodology to select a subset of variables to measure and monitor for multivariate statistical process control (SPC). In contrast with most dimensionality reduction approaches for SPC, we reduce the number of variables that must be measured, thereby reducing the time and cost associated with inspection. We develop a two-stage procedure that selects the variables in a manner that retains as much information on the full set of variables as possible. In the first stage, the variables are sorted according to some measure of information content, which has broad applicability outside of SPC. In the second stage, the selected variables are determined using two alternative tools. The first tool is a general criterion based on the amount of residual information in the nonselected variables. The second tool is based on the performance of the control chart in detecting simulated out-of-control events. We illustrate the usefulness of the approach with simulation results and a real metal-forming application.