Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines (SVMs). The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation (BP) neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMs-tyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simulation. Various methods of tire modeling are implemented from pure theoretical to empirical or semi-module models based on experimental results. A new way of representing tire data obtained from measurements is presented via support vector machines (SVMs). The feasibility of applying SVMs to steady- state tire modeling is investigated by comparison with three-layer backpropagation (BP) neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modeling the tire characteristics with better generalization performance. The SVMs-tire is implemented in The SVMs-tire can be a competitive and accurate method to model a tire for vehicle dynamics simulation.