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The detection of periodical defects is of primary importance in the manufacturing of many long flat products. As an example, a thick steel block is rolled (passed through several pairs of rolls) to obtain a long steel strip. When a roll has a flaw, it provokes a periodical defect on the strip. If the defect is not detected promptly, a large number of manufactured strips will be marked with the periodical defect. The economic losses incurred when roll flaws are not detected are very high, because the strips can not be sold to the customers and all the resources consumed in their manufacturing are wasted. This paper presents an algorithm for detecting periodical defects by analyzing the single defects detected by an inspection system based on computer vision. Because these defects form a periodical pattern, pattern matching techniques can be used for their detection. The paper also contains an analysis of the metrics used to characterize the performance of the algorithm and the experimental methodology used to find the optimal values for its configuration parameters. The optimal configuration must maximize true detections, and also minimize false detections. False detections can shut the manufacturing line down unnecessarily to search for non-existent roll flaws. Finally, the results obtained are compared with those provided by the most widely used system in this field. In most cases, the results provided by the algorithm proposed were better.