We describe a memory-based classification architecture for word sense disambiguation and its application to the SENSEVAL evaluation task. For each ambiguous word, a semantic word expert is automatically trained using a memory-based approach. In each expert, selecting the correct sense of a word in a new context is achieved by finding the closest match to stored examples of this task. Advantages of the approach include (i) fast development time for word experts, (ii) easy and elegant automatic integration of information sources, (iii) use of all available data for training the experts, and (iv) relatively high accuracy with minimal linguistic engineering.