A backpropagation neural network is trained to learn the inverse dynamics model of a temperature control system and then configured as a direct controller to the process. The ability of the neural network to learn the inverse model of the process plant is based on input vectors with no a priori knowledge regarding dynamics. Based on these characteristics, the neural network is compared to a conventional proportional-plus-integral (PI) controller. Experimental results show that the neural network controller performs very well and offers worthwhile advantages.