Abstract: Model-based functional approximation techniques are being used on in-line mixing processes affected by controlled variables which depend strongly on complementary physical variables. The aim for such type of nonlinear control problems is to compute the proportions of input product flow rates yielding a final product thus satisfying as much physical properties as manipulated input flow rates exists. The core of the contribution is based on functional approximation implemented by means of backpropagation neural networks associated to the computed multivariable control strategy on a viscosity control problem.