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For accurate and real-time detection of wafer surface defects, an online detection algorithm based on principal component analysis (PCA) and Bayesian probability model (BPM) is proposed. Firstly, the bilateral filtering method is improved to filter the noise in the wafer surface image and to highlight the pattern characteristics of the wafer surface. Next, the Hu invariant moments, histogram of oriented gradients (HOG) and scale invariant feature transform (SIFT) features of wafer surface defects are extracted. Then, the PCA method is adopted to reduce the feature dimension. Finally, the BPMs of the various defect patterns are constructed in the off-line modeling phase. In the on-line detection phase, the defect patterns are judged by using the Winner-take-all (WTA) method, and the BPM of the new defect patterns are constructed. The detection accuracy of the proposed algorithm is 80.6% in the WM-811K wafer database. The average detection time of single image is 40.5ms. The experimental results show that the proposed algorithm has high detection accuracy and is provided with real-time performance. It can be really applied to the on-line detection of wafer surface defects in the manufacturing line of integrated circuits.