Dynamic matrix control (DMC) is based on two assumptions which limit the feedback performance of the algorithm. The first assumption is that a stable step response model can be used to represent the plant. The second assumption is that the difference between the measured and the predicted output can be modeled as a step disturbance acting on the output. These assumptions lead to the following limitations : 1. Good performance may require an excessive number of step response coefficients. 2. Poor performance may be observed for disturbances affecting the plant inputs. 3. Poor robust performance may be observed for multivariable plants with strong interactions. Limitations 1 and 2 apply when the plant’s open-loop time constant is much larger than the desired closed-loop time constant. Limitation 3 is caused by gain uncertianty on the inputs. In this paper we separate the DMC algorithm into a predictor and an optimizer. This enables us to highlight the DMC limitations and to suggest how they can be avoided. We demonstrate that a new model predictive control (MPC) algorithm, which includes an observer, does not suffer from the listed limitations.