Objective To get a classifier model based on support vector machine (SVM) for printing defects detection. Methods The characteristics of printing defects were extracted according to human vision characteristics, and which were then analyzed. After that these feature data were imported into SVM for learning and training. The SVM classifier was then used to test the defect images. Results The classification accuracy rate of the classifier for point defects and planar defects reached 100%, and the classification accuracy rate for linear defects was 93.94%. Conclusion The classification method based on SVM could meet the demand of printing defect detection.