Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial processes, they experience a deficiency in knowledge acquisition and rely to a great extent on empirical and heuristic knowledge which in many cases cannot be objectively elicited. Among the usual problems to be resolved in fuzzy controller design include the determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. Some of these problems can be solved by application of machine learning. First, it is desirable to simplify and automate the specification of linguistic rules. Secondly, it is also desirable that modifications of control rules are possible in order to cope with peviously unknown, or changes in process dynamics. Machine learning methods have in recent years emerged from the use of learning algorithms modelled on natural and biological systems. These methods represent a means to abstract the advanced mechanisms of learning exhibited by such systems, which can, consequently, be applied to intelligent control. One of these new algorithms is the genetic algorithm which is modelled on the processes of natural evolution. This paper develops the application of online genetic algorithm techniques for fuzzy adaptive control. It is shown that although GA like other search techniques would not, in general, be suitable for real-time control their application is feasible within the structure of an adaptive fuzzy controller. Genetic algorithms are used to automate the acquisition of control rules and introduce objective criteria in the modification. Simulation show that satisfactory control of a nonlinear process is obtained using only very ’weak’ feedback signals.