A new procedure for predicting dead volume and bypassing in reactors was explored. The method is specific for processes already implemented with a linear reference model. It is based on using a neural network (NN) to obtain relationships between the parameters of the linear model and the dead volume and bypassing. Several experiments with bench scale reactors were carried out and the dead volume and bypassing were found by using classical flow models. By computer simulation we studied the combination bf a NN and the linear model of a CSTR with dead volume and bypassing. The NN is a three-layered perception, with sigmoid processing element and back-propagation learning. The input layer receives the parameters of the linear model and the output layer provides the predicted dead volume and bypassing. The accuracy of the trained NN was verified by presenting unseen data to the NN. The prediction errors are less than 15%.