This study presents an artificial neural network (ANN) approach for the modeling and control of the Fischer-Tropsch synthesis (FTS) slurry reactor. Operating data collected from an FTS demonstration plant were used to develop a radial basis function neural network (RBFNN) model, which is used for predicting the reactor temperature under industrial operation conditions. Additionally, a modified PID neural network (MPIDNN) control method was proposed for the reactor temperature control based on the trained RBFNN model. The differential evolution (DE) algorithm was used as the learning algorithm to automatically optimize the RBFNN and the PIDNN parameters. In the FTS slurry reactor simulation, the RBFNN model achieved satisfactory predictions of the reactor temperature, whereas the MPIDNN control system demonstrated an impressively stable and rapid control of the reactor temperature.