Column flotation processes are difficult to control because they are multivariable, difficult to model and complex systems. Model predictive control is a model-based control strategy that has been applied to a large number of industrial processes, where a sequence of future control actions is computed by minimizing an objective function. Accurate nonlinear models using soft computing (e.g. fuzzy and neural) techniques are increasingly being used in model based control. In this paper, model predictive control is applied to a column flotation process using a nonlinear fuzzy model. The process has four manipulating variables: feed flow rate, washing water, air and rejected flow rates. The outputs of this model, which are normally used to control the grade and the recovery in the flotation column, are the froth layer height, the bias flow rate and the air holdup in the collection zone. The most important controlled variable is the froth layer height which in this work has a very good performance.