We present a connectionist approach for solving Tangram puzzles. Tangram is an ancient Chinese puzzle where the object is to decompose a given figure into seven basic geometric figures. One connectionist approach models Tangram pieces and their possible placements and orientations as connectionist neuron units which receive excitatory connections from input units defining the puzzle and lateral inhibitory connections from competing or conflicting units. The network of these connectionist units, operating as a Boltzmann Machine, relaxes into a configuration in which units defining the solution receive no inhibitory input from other units. We present results from an implementation of our model using the Rochester Connectionist Simulator.