In this study we employed a real-time estimation method for ground vehicle parameters based on a generalized polynomial chaos (gPC) approach applied to an extended Kalman filter (EKF) technique. The vehicle models considered were a load transfer model (LTM) and a modified load transfer model (MLTM). The data used for performing the parameter estimation was collected on a Land Rover Defender 110. Two sets of data were collected: one on a rural road and one on an urban road. The mass of the vehicle, as well as the lateral and longitudinal location of the center of gravity (CG), was estimated. The reason for developing the MLTM was that it has a significantly reduced need for prior knowledge about the CG location. The LTM requires that sensors be placed at the CG of the vehicle. Obviously, if that is already known, then this whole scheme is unnecessary. The MLTM is designed to remove that requirement through the ability to estimate the CG accelerations through the accelerometers placed at the four corners of the vehicle. The models and methods presented are validated against real data and parameters values with high accuracy. The computational cost is reasonable, and the estimator runs faster than real-time.