In this paper, we present a model predictive inferential control (MPIC) strategy to address the problem of controlling unmeasured output variables (such as quality) using readily available secondary measurements. First we establish the relationship between inferential control and other classical control strategies such as cascade and internal model control. Next we present a framework for incorporating the inferential control strategy within the framework of the often used model predictive control (MPC). This framework, termed model predictive inferential control (MPIC), is general enough to accommodate multiple secondary measurements as well as nonlinear estimators and controllers. The advantages of inferential control are established using two case studies. One is the Shell challenge problem which employs linear transfer function models. The second is a nonlinear, multivariable problem on the control of product composition using secondary measurements on a simulated injection pultrusion process. Problems of collinearity are addressed using principal component analysis (PCA) during the construction of the dynamic estimator. These simulations demonstrate the advantages of the proposed model predictive inferential control strategy.