Researchers have long been studying the effects of the modification of friction material compositions on their tribological properties. Predictive models have also been developed, but they are of limited use in the design of new compositions. Therefore, this research aims to investigate the tribological behaviour of single ingredients in friction materials to develop a tribological dataset. This dataset could then be used as a foundation for a cellular automaton (CA) predictive model, intended to be a tool for designing friction materials. Tribological samples were almost entirely composed of four distinct friction material ingredients, and one sample composed of their mixture was successfully produced. Pin-on-disc (PoD) tribometer testing and scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDXS) analysis were used for the tribological characterization. Each material showed distinct tribological properties and evolution of the contact surface features, and the synergistic effect of their mutual interaction was also demonstrated by their mixture.