Wear of brake friction materials were found to be a complex combination of abrasion, adhesion, fatigue, delamination, and thermal decomposition. Stochastic nature of wear of brake friction materials is result of these wear mechanisms and their transition from one combination to another. The dominant wear mechanism of brake friction materials is influenced by braking regimes and friction material characteristics. Regarding friction material characteristics, the most important influences are related to its formulation and manufacturing conditions. Prediction of friction materials wear versus their manufacturing conditions can be considered as an important issue for further friction materials development. In this paper, the artificial neural network abilities have been used for predicting wear of the friction materials versus synergistic influence of: (i) mass percentages of phenolic resin, fibers, abrasives, and lubricants in formulation of the friction materials, (ii) work done by brake application, (iii) brake interface temperature, and (iv) friction materials manufacturing conditions (moulding pressure, moulding time, moulding temperatures, heat treatment time, and heat treatment temperatures). The model of the friction materials wear has been developed able to predict how manufacturing conditions of the friction material influence its wear in synergy with different mass percentages of key ingredients in the friction material formulations at different brake interface temperatures.