Automatic industrial surface inspection methodology based on Magnetic Particle Inspection is developed from image acquisition to defect classification. First the acquisition system is optimized, then tubular material images are acquired, reconstructed then stored. The characteristics of the crack-like defects with respect to its geometric model and curvature are used as a priori knowledge for mathematical morphology and linear filtering. After the segmentation and binarization of the image, vast amount of defect candidates exist. Finally classification is performed with decision tree learning algorithm due to its robustness and speed. The parameters for mathematical morphology, linear filtering and classification are analyzed and optimized with Design Of Experiments based on Taguchi approach. The most significant parameters obtained may be analyzed and tuned further. Experiments are performed on tubular materials and evaluated by its accuracy and robustness by comparing ground truth and processed images. The result is promising with 97 % True Positive and only 0.01 % False Positive rate on the testing set.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.