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In the ceramic tile manufacturing industry, the quality of production achieved depends to a large extent on the quality of the tile, which is very important for its classification and price. Currently, this process is performed by human operators, but many industries aim to improve performance and production through automation of this process. In this work, we present the development of a platform based on an artificial vision that allows the identification of defects in ceramic tiles, so that we can classify them according to their quality. The algorithms chosen to develop the platform are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In order to implement these algorithms, the images are preprocessed, the descriptors for defect detection are obtained, then the algorithms are used and the results obtained

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