This paper explores several novel approaches to solve the Morris water maze task. In this spatial memory task, the robot must learn how to associate perceptual information with a particular location to aid in navigating to the goal. A self-organizing feature map (SOFM) is used to discretize the perceptual space. The robot must then learn to associate these perceptual states with an action used to navigate through the environment. Two navigational approaches are proposed. The first approach involves computing a probabilistic graph between SOFM nodes and then searching the graph to locate a path to the goal. The second approach uses temporal difference learning to learn the association between an SOFM node and an action that will direct it to the goal. The paper compares the effectiveness of these two approaches and discusses their respective utility.