During the winter season, contamination of runway surfaces with snow, ice, or slush causes potential economic and safety threats for the aviation industry. The presence of these materials reduces the available tire–pavement friction needed for retardation and directional control. Therefore, pilots operating on contaminated runways need accurate and timely information on the actual runway surface conditions. In order to validate existing models for assessing runway conditions, or to create models from machine learning, it is necessary to estimate runway friction. We show how this can be done using flight data from airplanes. Data such as longitudinal acceleration, airspeed, ground speed, flap settings, engine speed and brake pressures are sampled at least each second during landings. However, converting these samples into reliable estimates of runway friction is far from trivial, and we discusses some of the challenges with this.