A self-organizing fuzzy controller (SOFC) has been developed to control complicated and nonlinear systems. However, it is arduous to choose an appropriate learning rate and a suitable weighting distribution of the SOFC to achieve satisfactory performance for system control. Furthermore, the SOFC is mainly used to control single-input single-output systems. When the SOFC is applied to manipulating a robotic system, which is an example of multiple-input multiple-output systems, it is difficult to eliminate the dynamic coupling effects between the degrees of freedom (DOFs) of the robotic system. To address the problems, this study developed a self-organizing fuzzy radial basis-function neural-network (RBFN) controller (SFRBNC) for robotic systems. The SFRBNC uses an RBFN to regulate in real time these parameters of the SOFC to optimal values, thereby solving the problem faced when the SOFC is applied. The RBFN has coupling weighting regulation ability, so it can eliminate the dynamic coupling effects between the DOFs for robotic system control. From the experimental results of the 6-DOF robot tests, the SFRBNC demonstrated better control performance than the SOFC.