Abstract: Experiment is always the prime method in forecasting SMT soldering reflow profile, while its high cost and low efficiency make the company hard to develop. According to the nonlinear relationship between the multi input and output, reflow profile forecast model based on BP neural network was proposed. The deficiencies in training, such as error calculating and weight adjusting, are improved to eliminate the sample order's impact on the network. Moreover, the network training speed is enhanced rapidly. MAPE assessment approach is carried out to compare network prediction with the production data of a company. The results shows that predicted error meets the demand of required precision. In conclusion, BP neural network is effective and efficient in the reflow profile prediction.