The accuracy of a robot arm is determined by its ability to move in a given particular task space to specific Cartesian positions that are not necessarily pretaught. As a consequence, the inverse kinematics is an important problem as it must be solved in real-time in order to position the end-effector at an appropriate Cartesian location. However, it is a difficult and challenging problem for it involves the determination whether or not at least one mathematical set of robot joint angle values exists that will produce a desired coordinate configuration. The mathematical solutions should be checked against the physical constraints associated with the manipulator. Many times, a solution many not be physically realizable in a constrained environment. The advent of artificial neural networks has made it possible to obtain general learning schemes which can be used to arrive at feasible solutions to inverse kinematics problem in a constrained environment independent of a robotic structure. In this paper, we present such a learning scheme using a dynamic neural processor (DNP). This neural model functionally mimics the subpopulation of biological neurons. For analytical simplicity, only two subpopulations of neurons, namely excitatory and inhibitory, are assumed to coexist. The DNP is a neural network structure consisting of two dynamic neural units coupled as excitatory and inhibitory neurons. It is demonstrated in this study that the DNP would avoid time consuming numerical calculations and provide, more or less, instant recall of the learned associations. The learning and adaptive nature of this neural approach is demonstrated for two- and three-linked robots.