A computer control algorithm utilizing the modern control theory and the heuristic artificial intelligence approach has been developed to control the MIT Scheinman electric arm. The control algorithm is task-oriented with the capabilities of accepting visual input coordination and discrete word voice commands in addition to the linguistic commands typed in by an operator at the remote terminal. The motion of the arm is considered to be composed of motion of the wrist and orientation of the hand. A human operator, always present in the control loop and interacting with the system, generates linguistic task-directed commands at the remote terminal. A task-directed command is recognized, interpreted, and decoded into sequence of subtasks. The first subtask corresponds to the motion of the wrist which is controlled by the suboptimal feedback controller. The remaining four subtasks are further broken down into combinations of six primitive movements which govern the position/orientation of the hand. The objective of the visual recognition algorithm is to identify ob-jects and their locations surrounding the arm from its environmental library or model. The library is then updated to initiate the arm to complete its execution of task. Areas and circumferences of the objects are the two features chosen for recognition. The recognition of an object is then based on the threshold value of the weighted sum of area and circumference of the object. The set of weights is trained again when a new object is introduced to the arm and incorporated into the library. The real-time implementation of the algorithm on AARL MIT arm connected to a PDP 11/45 computer shows that it can recognize the objects surrounding the arm within 50 seconds.