The need for a method to design optimal engine mounting systems is the motivation for the work described in this paper. The paper discusses several vehicle ride quality criteria and the advantages and disadvantages of different performance measures. The problem of vehicle modeling and ride quality optimization through engine mount design is formulated within the framework of Linear Quadratic Gaussian (LQG) control theory using an Extended Kalman Filter (EKF) to identify vehicle parameters and to estimate the states. This estimation identification technique is demonstrated using a simple model. A 16 degree-of-freedom (DOF) lumped mass vehicle model is described, characterizing the natural frequencies of the engine, cab, and the frame, to be used as a baseline model in describing the vehicle motion.