using an error back propagation algorithm similar to the BP algorithm for artificial neural networks. The fault diagnosis models don’t vary with the change of the network structure, so they can be applied to any transmission power system. Furthermore, they have clear semantics, rapid reasoning, powerful error tolerance ability and no convergence problem during the diagnosing procedure. Experimental tests show that the approach is feasible and efficient, so the prototype program based on the approach is promising to be used in a large transmission power system for on-line fault diagnosis.