This paper presents a novel approach for process control that uses neural networks to model the steady-state inverse of a process which is then coupled with a simple reference system synthesis to generate a multivariable controller. The control strategy is applied to dynamic simulations of two methanol-water distillation columns that express distinctly different behaviour from each other (one simulates a lab column, while the second simulates an industrial-scale high-purity column). A steady-state process simulation package was used to generate all the neural network training data. An efficient training algorithm based on a nonlinear least-squares technique was used to train the networks. The neural network model-based controllers show robust performance for both setpoints and disturbances, and performed better than conventional feedback proportional-integral (PI) controllers with feedforward features.