The self-organizing feature maps developed by Kohonen appear to capture some of the advantages of the natural systems on which they are based. A summary of the operation of this form of artificial neural network is presented. It was concluded that the primary benefits of using self-organizing feature maps result from their adaptability and plasticity while most problems are largely caused by the lack of a rigorous mathematical foundation. Two different robotics applications are described. In the first, developed by Martinez and Schulten, a hierarchical structure composed of many self-organizing feature maps is used to control a five degree of freedom robot arm. While it was noted that there may be some practical problems, the general idea of using a hierarchical structure appears sound and may be applicable to a wider range of problems. The second robotics application was developed by Saxon and Mukherjee. They used a single self-organizing feature map to learn the motion map of a two degree of freedom arm. The use of such a system should simplify path planning by combining multiple constraints into a 2-D structure.