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Management of unstructured production data is a key challenge for Industry 4.0. Effective product tracking endorses data integration and productivity improvements throughout the manufacturing processes. Radio-frequency identification (RFID) tags are used in many tracking cases, but in some manufacturing environments, those cannot be used as they might get damaged or removed during processing. In this paper, we propose an alternative visual product tracking system. The physical system uses two cameras placed at the two ends of the tracked process(es). Product pairs are then matched with a Siamese neural network operating on the product images and trained offline on the problem at hand with labeled data. The proposed system can track products solely based on their visual appearance and without any physical interference with the products or production processes. Unlike other existing image-based methods, the proposed system is invariant to major positional and visual changes in the products. As a proof-of-concept, we tested the proposed system with real plywood factory data and were able to track the products with 98.5 &#x0025; accuracy in a realistic test scenario. The implementation of the proposed method and the Veneer21 dataset are publicly available at <uri>https://github.com/TuomasJalonen/visual-product-tracking-system</uri>.