A neural networks controller is developed and used to regulate the temperatures in a crude oil distillation unit. Two types of neural networks are used; neural networks predictive and nonlinear autoregressive moving average (NARMA-L2) controllers. The neural networks controller that is implemented in the neural network toolbox software uses a neural network model of a nonlinear plant to predict future plant performance. Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. A comparison has been made between two methods to test the effectiveness and performance of the responses. The results show that a good improvement is achieved when the NARMA-L2 controller is used with maximum mean square error of 103.1 while the MSE of neural predictive is 182.7 respectively. Also shown priority of neural networks NARMA-L2 controller which gives less offset value and the temperature response reach the steady state value in less time with lower over-shoot compared with neural networks predictive controller.