Abstract: The electrode displacement and dynamic resistance signals of resistance spot welding process are collected synchronously.Through the time-domain analysis of electrode displacement signal in the welding process, nine characteristic parameters relating to weld quality are picked up to set up a set of data which characterizes the input samples, on the basis of the different phase of nugget forming marked by simultaneous dynamic resistance signal.The principal component analysis(PCA) to remove the self-correlation of input characteristics and realize dimensionality reduction is integrated with the conventional method of support vector machine(SVM), while the shear strength of welded spot was taken as the evaluation index of welded spot quality.The comparison of predicted results under PCASVM and conventional SVM by means of cross-validation test shows that PCA-SVM algorithm improves the generalization ability and the predicted accuracy of SVM method.