A neural network trained with responses to a step input in concentration for a vessel with various percentages of perfect mixing, short circuiting. and distance-velocity lag classifies accurately flow patterns that the program has not encountered previously. Flow responses digitized at a selected sequence of times were the inputs and the percentages of three modes of flow were the outputs for a commercial program for neural networking. Default values of the program worked quite well for the layout of the network and for the convergence error for training, and tweaking these values had little effect on training time or performance.