Robots with vision capability have been taught to recognize unknown objects by comparing their shape features with those of known objects, which are stored in the vision system as a knowledge base. Traditionally, this knowledge base is created by showing the robot the set of objects that it is likely to come across. This is done with the vision system to be used and must be done in an online mode. An approach to teach the robot in an off-line mode by integrating the robot vision system and an off-line graphic system, has been developed in this research. Instead of showing the objects that the robot is likely to come across, graphic models of the objects were created in an off-line graphic system and a FORTRAN program that processes the models to extract their shape parameters was developed. These shape parameters were passed to the vision system. A program to process an unknown object placed in front of the vision system was developed to extract its shape parameters. A program that compares the parameters of the unknown object with those of the known models was also developed. The vision system was calibrated to measure the pixel dimensions in inches. In the vision system, shape parameters of the objects were found to vary with different orientations. The range of variation for each parameter was established and this was taken into consideration in the parameter comparison program. / Master of Science