Application of artificial neural networks in simulation of chemical engineering unit operations are studied. On the basis of the present work it is suggested that the raw data describing the phenomenon should be processed so that the relationships of the input variables are studied and if relationships are found, such as dimensionless numbers, these relationships are set as inputs in the neural network. Further, if the phenomenon can be described by a simplified mathematical expression, the value calculated by such simplified solution is set as one of the inputs. The method is demonstrated by three examples, namely, by turbulent fluid flow in a tube, breakthrough curve of adsorption, and by suspension crystallization. In all cases the method presented in this work results in a more accurate simulation and in easier training of the neural network.