In Taguchi method, a preferred factor solution can be derived by factional factor experiments and factor level response analysis. However, the experimental error caused by factor levels orthogonality in orthogonal arrays is inevitable. In order to improve this, a network approach combines with Taguchi's method by progressive training is proposed, which integrates the experimental orthogonality and the learning ability of neural network to establish an inferring network model. Two cases of IC leadframe dam-bar shearing have carried out to demonstrate the modeling process. By increasing a few additional experiments, an optimal factor level combination can be inferred, which is more objective and accurate than the traditional Taguchi's method does.