This paper applies extended Kalman-Bucy filtering (EKBF) and Bayesian hypothesis selection to estimate motion, tire forces, and road coefficient of friction (mu) of vehicles on asphalt surfaces. The EKBF estimates the state and tire forces of an eight-degree-of-freedom vehicle from vehicle-mounted sensors. The filter requires no a priori knowledge of mu and does not require a tire force model. Resulting force, slip, and slip angle estimates are compared statistically with those that result from a nominal analytic tire model to select the most likely mu from a set of hypothesized values. The methods have application to both off-line construction of tire models and development of vehicle control systems that require mu. Both simulation results and results of applying estimation methods to field test data are presented. Simulation results show excellent convergence and accuracy of mu estimates, and results of processing held test data demonstrate the ability to construct useful tire models. Computation and sensor requirements, and robustness of the mu identification algorithm are considered.