Model predictive control (MPC) schemes such as MOCCA, DMC, MAC, MPHC, and IMC use discrete step (or impulse) response data rather than a parametric model. They predict the future output trajectory of the process {ŷ(k + i), i = 1, …, P}, then the controller calculates the required control action {Δu(k + i), i = 0, 1, …, M − 1} so that the difference between the predicted trajectory and user-specified (setpoint) trajectory is minimized. This paper shows how the step (impulse) response model can be put into state space form thus reducing computation time and permitting the use of state space theorems and techniques with any of the above-mentioned MPC schemes. A series of experimental runs on a simple pilot plant shows that a Kalman filter based on the proposed state space model gives better performance that direct use of the step response data for prediction.