We postulate that early childhood language semantics is "grounded" in perceptual/motor experiences. The DETE model has been constructed to explore this hypothesis. During learning, DETE's input consists of simulated verbal, visual and motor sequences. After learning, DETE demonstrates its language understanding via two tasks: (a) Verbal-to- visual/moior association -- given a verbal sequence, DETE generates the visual/motor sequence being described, (b) Visuallmotor-io-verbal association -- given a visual/motor sequence, DETE generates a verbal sequence describing the visual/motor input. DETE ' s learning abilities result from a novel neural network module, called katamic memory. DETE is implemented as a large-scale, parallel, neural/ procedural hybrid architecture, with over 1 million virtual processors executing on a 16K processor CM - 2 Connection Machine.*