In this study, a novel adaptive hybrid fuzzy system-wavelet neural network (FS-WNN) approximator is developed for control of uncertain non-linear multiple-inputs and multiple-outputs systems. The main idea is to use two different universal approximators to approximate each unknown non-linear function of the dynamic system. The first approximator is a FS and the second approximator is a WNN. Each approximator approximates the system's unknown functions independently. Then, the two approximations of the same function are combined using a modulation technique. The used modulation attributes to the first approximation a scaling factor and attributes its complementary factor to the second approximation to synthesise a hybrid optimal approximation of the function. Thus, system unknown functions are accurately approximated using the novel adaptive hybrid approximator that gathers the advantages of both approximators. The adaptation laws of the developed hybrid approximator are derived using Lyapunov's direct method to ensure the stability of the closed loop system. A simulation study is given to evaluate the performance of the proposed hybrid FS–WNN approximator in comparison with the FS and WNN approximators in indirect adaptive control of a two-link robot manipulator.