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Abstract: This paper is concerned with the optimal design of motion control for scan tracking measurement using Cerebellar Model Articulation Controller (CMAC) neural networks in order to improve the measuring efficiency while maintaining the measurement accuracy, and to achieve friction compensation. Aiming at the effect of geometric shape and material friction of the model surface on the precision and efficiency of scan tracking measurement, technologies of model surface geometric and friction feature identification and quantification are researched. A novel optimal motion controller for scan tracking measurement is designed and realized, which automatically predicts the surface features (including geometric feature and friction feature) and adjusts the scan tracking velocity in advance. The approach to friction quantification and compensation in measuring process is given specifically. Finally, through Matlab simulation experiments, the realizability and application effect of the studied optimal motion control method for scan tracking measurement are verified, and the measuring efficiency is increased by 123.33%. Simulation results show that the proposed motion controller is an effective way to enhance the measurement efficiency remarkably compared with the traditional control strategy. [Copyright &y& Elsevier]