This paper presents a design methodology for predictive control of industrial processes via recurrent fuzzy neural networks (RFNNs). A discrete-time mathematical model using RFNN is constructed and a learning algorithm adopting a recursive least squares (RLS) approach is employed to identify the unknown parameters in the model. A generalized predictive control (GPC) law with integral action is derived based on the minimization of a modified predictive performance criterion. The stability and steady-state performance of the resulting control system are studied as well. Two examples including the control of a nonlinear process and the control of a physical variable-frequency oil-cooling machine are used to demonstrate the effectiveness of the proposed method. Both results from numerical simulations and experiments show that the proposed method is capable of controlling industrial processes with satisfactory performance under setpoint and load changes. (C) 2006 Elsevier Ltd. All rights reserved.