This paper presents a neural network based fault diagnosis approach for analog circuits, taking the tolerances of circuit elements into account. Specifi-cally, a normalization rule of input information, a pseudo-fault domain border (PFDB) pattern selection method and a new output error function are proposed for training the backpropagation (BP) network to be a fault diagnoser. Experi-mental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy, and provides at Ieast an order-of magnitude improvement in post-fault diagnostic speed. This paper presents a neural network based fault diagnosis approach for analog circuits, taking the tolerances of circuit elements into account. Specifi-cally, a normalization rule of input information, a pseudo-fault domain border (PFDB) pattern selection method and a new output error-function are proposed for training the backpropagation (BP) network to be a fault diagnoser. Experi-mental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy, and provides at Ieast an order-of magnitude improvement in post-fault diagnostic speed.