Artificial neural networks exhibit a great potential for both model based control and software sensing due to their non-linear identification capabilities. This paper proposes the use of adaptive neural networks applied to the prediction of product composition starting from secondary variable measurements, and to both dual composition control and inventory control for a continuous ethanol-water nonlinear pilot distillation column monitored under LabVIEW. A principal component analysis based algorithm has been applied to select the optimal net input vector for the soft sensor. Genetic algorithms are used for the automatic choice of the optimum control law based on a neural network model of the plant. The proposed real time control scheme offers a high speed of response for changes in set points and null stationary error for both dual composition control and inventory control, and reveals the potential use of this control strategy when an experimental multivariable set-up is addressed. (C) 2012 Elsevier Ltd. All rights reserved.