An adaptive control law incorporating a biologically inspired neural networks for robot control is used as a mathematical model of human motor control and the motor control adaptation. Modeling human motor control strategy is made difficult due to the redundancies in the human motor control system. This control model is able to overcome the difficulties of the human motor control modelling, and include the learning capability of the motor control strategy which was omitted in human motor control studies until now. By adaptively piecing together a collection of elementary computational elements, the proposed model develops complex internal models which are used to compensate for the effects of externally imposed forces or changes in the physical properties of the system. In order to examine the form of human motor control adaptation in detail, a computer simulation was developed with a two dimensional model of the human arm which utilized the proposed adaptive motor control model. The simulation result show that the model is able to capture the characteristics of the motor control adaptation seen in human experiments reported by [14], [46]. For cont inuation of this research, an experimental apparatus was designed and built for the human motor control study. This apparatus is a cable driven, two-dimensional manipulator which is used to apply specified disturbance forces to the human arm. The preliminary experiment conducted with this test apparatus show a strong correlation to the simulation data and other experimental data reported on human reaching motions.