To classify the quality of the resistance spot welding process, the relationship between the welder electrode displacement curve characteristics and the weld shear force has been explored where 11 statistical features of the displacement signals are extracted to represent the welding quality.SOM(Self-Organizing Mapping) neural networks have been employed to discover their quantitative relationship.In order to identify the influences of various displacement curve features, all of the available combinations have been used as inputs for SOM neural networks, respectively.Further combined analyze the impact of each feature on the classification results, yielding the best quality-indicative combination of characteristics.We find there is no determinant relationship between the welding quality and the level of expulsion rate.The maximum electrode displacement, the span of welding process and the centroid of the electrode displacement curve are most impact the quality of welding.The experiments show that SOM is feasible to assess the welding quality and render visualized intuitive evaluation results.