This paper presents a model predictive control (MPC) algorithm utilizing a state-space model representation, allowing for the inferential control of unmeasured process states using a prioritized control objective formulation. Knowledge of the unmeasured states is gained through the use of an external state estimation routine, while mixed-integer methods are used to implement the prioritization of the control objectives. The capabilities of the algorithm are demonstrated on an experimental multivariable air pressure tank system.