To solve the problems of real-time feature extraction on weld surface images,a feature evaluation algorithm based on incremental block principal component analysis (IBlockPCA) is proposed.First,the weld surface images are segmented into sub-image blocks,and then the blocks are reconstructed.Next,the incremental feature extraction is performed on the local block images by using the proposed IBlockPCA,and the KNN is used to classify and recognize the evaluated principal components.Finally,the performances are compared on the weld dataset.The experimental results show that the IBlockPCA is superior to other principal component analysis (PCA) algorithms in the convergence rate,classification rate and complexity.The classification rate is 97.5%,and the average processing speed can reach 50 frames per second.It can meet the real-time processing requirements of weld surface images.