The aim of the paper is to approach the study of wear on a metallic surface in the case of dry linear contact, plastic material reinforced with short glass fibres (SGF) on surfaces of C120 and Rp3 steel, through the method of artificial neural networks (ANN). This is because wear processes involve very complex and powerfully nonlinear phenomena. Consequently, analytic models are difficult or impossible to obtain. This is also necessary due to the multiple inputs (normal load – contact pressure, relative sliding speed, measured contact temperature, materials properties) and outputs (width and depth of the wear scar, measured contact temperature) which influence each other continually. A multitude of experimental tests was performed with different loads and speeds, which have led to some conclusive results but, in some cases, with relatively high variance. Therefore, the paper aims to use the same experimental data in an ANN – based approach, which is a state-of-the-art modelling method, due to its properties for learning, generalisation and nonlinear behaviour, adequate to plastic materials armed with short glass fibres. The innovative approach is compared with a baseline model featuring multivariate linear regression optimised using gradient descent.