Abstract: In this paper, the rule extraction of neural networks is disscussed, which is an effective method to avoid the shortcoming of being “black boxes”. Techniques based on decompositional and input output mapping approaches are studied and their fundamental concepts and evaluates their performances are generalized. Based on similar weight approach, the CSW approach is proposed to efficiently solve the rule extraction from continuous\|input neural networks. CSW is applied in IRIS Flower Classification Problem,and experiment results show that rules extracted by our method are accurate and comprehensible.