In this paper, we present a printed circuit board (PCB) inspection system based on using Hausdorff distance for image alignment and defect detection. In addition, we apply support vector machine (SVM) for the defect classification and the metal classification in this system. The three major components in the proposed PCB inspection system consist of image alignment, defect detection, and defect classification. In image alignment, a coarse-to-fine search technique is applied to accelerate the speed of finding the minimal Hausdorff distance between the reference and the inspection images. For defect detection, we calculate the Hausdorff distance of every pixel in the inspection image as the first step and compare the result with a predefined threshold. For the cases where the computed Hausdorff distance is greater than the threshold, the location of that pixel is labeled as a defect suspect. The existence of defect then can be confirmed by merging the nearby suspects into one object. For defect classification, the local image features are extracted and passed to support vector machine for training and identifying defect types. In this work, we focus on distinguishing the type of a defect as one of open, short, pinhole, over-etch, or under-etch types. Support vector machine can be applied to metal classification as well. At the current stage, we supply support vector machine with RGB color information as the feature vector for metal classification. Experimental results show that the Hausdorff distance based method detects defects in a printed circuit board efficiently and accurately, and the support vector machine approach also gives satisfactory results for both defect and metal classifications.