In this paper, we combine the advantages of fuzzy logic and neural network techniques to develop an intelligent control system for processes having complex, unknown and uncertain dynamics. In the proposed scheme, a neural fuzzy controller (NFC), which is constructed by an equivalent four-layer connectionist network, is adopted as the process feedback controller. With a derived learning algorithm, the NFC is able to learn to control a process adaptively by updating the fuzzy rules and the membership functions. To identify the input-output dynamic behavior of an unknown plant and therefore give a reference signal to the NFC, a shape-tunable neural network with an error back-propagation algorithm is implemented. As a case study, we implemented the proposed algorithm to the direct adaptive control of an open-loop unstable nonlinear CSTR. Some important issues were studied extensively. Simulation comparison with a conventional static fuzzy controller was also performed. Extensive simulation results show that the proposed scheme appears to be a promising approach to the intelligent control of complex and unknown plants, which is directly operational and does not require any a priori system information.