Mount development and optimization plays an important role in the NVH refinement of vehicle as they significantly influence overall driving experience. Dynamic stiffness is a key parameter that directly affects the mount performance. Conventional dynamic stiffness evaluation techniques are cumbersome and time consuming. The dynamic stiffness of mount depends on the magnitude of load, frequency of application and the working displacement. The above parameters would be far different in the test conditions under which the mounts are normally tested when compared to operating conditions. Hence there is need to find the dynamic stiffness of mounts in actual vehicle operating conditions. In this paper, the dynamic stiffness of elastomeric mounts is estimated by using a modified matrix inversion technique popularly termed as operational path analysis with exogenous inputs (OPAX).The test vehicle is an all-wheel drive (AWD) monocoque vehicle which has power train in east-west configuration and a rear drive module (RDM) used to transfer the drive to rear wheels. The dynamic stiffness values of power train mounts, front sub frame and rear sub frame mounts and RDM mounts are determined by OPAX method. The result is used in building a simulation model to analyze vehicle driveline NVH. The new method has increased the efficiency of computation by reducing the number of FRF measurements. OPAX method takes into account the multiple order based data unlike matrix inversion method. Guidelines for the selection of operating conditions and selection of orders are discussed to improve the quality of simultaneous equations and to reduce the ill-conditioning of matrix. The influence of assumption of constant stiffness over certain band width on the stiffness pattern is analyzed. Drawbacks of the method in estimating the stiffness in some of the paths where force level and attenuation level are negligible are discussed. The predicted dynamic stiffness data is also used in finding the dynamic forces in other operating conditions of the vehicle.