Slip control is a common and crucial functionality for a number of vehicles ranging from cars to tractors and trucks. The purpose of slip control is to improve vehicle's traction and motion stability, prevent excessive wheel slippage and provide stable braking. Slip is a non-linear function of the vehicle's ground speed and wheel rotation frequency and as such depends on the internal state variables, such as wheel load torque, which are in turn unknown. A number of approaches exist to estimate the unknown state, one of the most used ones being based on Kalman filter. In the current study, we present experimental results of slip control for an electrical single wheel-drive tractor using an unscented Kalman filter, which is a variant of Kalman filter suitable for non-linear systems. To cope with the problems of state estimation for heavy-duty vehicles, the Kalman filter was augmented with a fuzzy-logic supervisor aimed at assessment of vehicle dynamics. The goal of the supervisor was to adapt the state noise covariance with the goal of improving tracking accuracy. Wheel slip reduction was observed and its mean stayed within the desired limit. Experiments were carried out under two different road conditions and the condition change was identified by the Kalman filter.