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This paper presents a new method for inspecting aluminum castings automatically from a sequence of radioscopic images taken at different positions of the casting. The classic image-processing methods for flaw detection of aluminum castings use a bank of filters to generate an error-free reference image. This reference image is compared with the real radioscopic image, and flaws are detected at the pixels where the difference between them is considerable. However, the configuration of each filter depends strongly on the size and shape of the structure of the casting under inspection. A new two-step technique is proposed to detect flaws automatically and that uses a single filter. First, the method identifies potential defects in each image of the sequence, and second, it matches and tracks them from image to image. The key idea of this paper is to consider as false alarms those potential defects which cannot be tracked in the sequence. The robustness and reliability of the method have been verified on both real data in which synthetic flaws have been added and real radioscopic image sequences recorded from cast aluminum wheels with known defects. Using this method, the real defects can be detected with high certainty. This approach achieves good discrimination from false alarms. Index Terms--Aiuminum castings, automated inspection, computer vision, flaw detection, image segmentation, X-ray testing.