Using associative memories and sparse distributed representations we have developed a system that can learn to associate words with objects, properties like colors, and actions. This system is used in a robotics context to enable a robot to respond to spoken commands like "bot show plum" or "bot put apple to yellow cup". The scenario for this is a robot close to one or two tables on which there are certain kinds of fruit and/or other simple objects. We can demonstrate part of this scenario where the task is to find certain fruits in a complex visual scene according to spoken or typed commands. This involves parsing and understanding of simple sentences and relating the nouns to concrete objects sensed by the camera and recognized by a neural network from the visual input.