A variety of vehicle control systems – from active safety to power management – greatly benefit from accurate, reliable, and robust estimation of vehicle mass and road grade. This paper develops a vehicle mass and road grade estimation scheme, termed parallel mass and grade (PMG) estimation, and presents the results of a study investigating its accuracy and robustness in the presence of various noise factors. An estimate of road grade is calculated by comparing the acceleration measured by an on-board longitudinal accelerometer with that obtained by differentiating the undriven wheel speeds, while mass is independently estimated by means of a longitudinal dynamics model and a recursive least squares (RLS) algorithm, using the longitudinal accelerometer to isolate grade effects. To account for the influences of acceleration-induced vehicle pitching on PMG estimation accuracy, a correction factor is developed from controlled tests under a wide range of throttle levels. The estimation approach is applied to data collected while driving on public roads under a variety of conditions, replicating several noise factors associated with daily driving. Torque and acceleration thresholds are developed to isolate driving events that are likely to very quickly converge to accurate mass estimates. The proposed method is shown to deliver estimates within 3% of true vehicle mass.