Fuzzy Inference Systems (FISs) and Artificial Neural Networks (ANNs), as two branches of Soft Computing Systems (SCSs) that pose a human-like inference and adaptation ability, have already proved their usefulness and have been found valuable for many applications [1], [2]. They share a common framework of trying to mimic the human way of thinking and provide an effective promising means of capturing the approximate, inexact nature of the real world process. In this paper we propose an Adaptive Neuro-Fuzzy Logic Control approach (ANFLC) based on the neural network learning capability and the fuzzy logic modeling ability. The approach combines the merits of the both systems, which can handle quantitative (numerical) and qualitative (linguistic) knowledge. The development of this system will be carried out in two phases: The first phase involves training a multi-layered Neuro-Emulator network (NE) for the forward dynamics of the plant to be controlled; the second phase involves on-line learning of the Neuro-Fuzzy Logic Controller (NFLC). Extensive simulation studies of nonlinear dynamic systems are carreid out to illustrate the effectiveness and applicability of the proposed scheme.