Abstract: The bonding quality test is the most important of all the tests performed on plywood, as it determines the suitability of boards for use in the type of exposure they are intended for. Because this test involves aging pretreatment, results are not available in less than 24-97 h after manufacture, depending on the type of board, and therefore any error in the manufacturing process is not detected until 1-4 days later. To solve this time problem, an artificial neural network was used as a predictive method to determine the suitability of board bonding through other properties that can be determined in less testing time: thickness, moisture content, density, bending strength and modulus of elasticity. The network designed, a feed forward multilayer perceptron trained by supervised learning after normalization of the input data, allowed the bonding test result to be predicted with 93% accuracy. This mathematical tool allows plywood bonding quality to be predicted through other properties that can be determined in less time, enabling errors in the production line to be detected quickly and reliably.