[[abstract]]The Cerebellar Model Articulation Controller (CMAC) is a lattice-basedAssociative Memory Network (AMN), which applies table look-uptechnique to address memory and quickly generate correct outputs forgiven inputs. Owing to its fast learning property, good localgeneralization capability, and easy of implementation by hardware.CMAC has been applied in many application such as robotic control,data recognition, pattern classification, and signal process. Theinput space quantization method determines the degree of approximationaccuracy and the efficiency of memory utilization directly. However, the conventional CMAC performs an equal-size method for the inputspace quantization without considering for the various distributionsof training data sets. In the paper, a hierarchical multi- resolutionquantization of CMAC is presented, the concept consists in applying aShannon's Entropy Measure for analyzing the distributions of trainingdata sets and the optimum quantization division of input space isdetermined appropriately based on the Fuzzy Inference. The approachwill be applied on a second-order sliding mode control system, fromexperimental results show that our proposed method is able to achievebetter learning accuracy, convergence speed, and memory utilizationefficiency compared to the conventional CMAC.