In this technical note, we introduce several novel vector measures of accuracy for sampled-data nonlinear models. The new definitions of truncation error assign a unique error bound to each component of the state vector. We argue that this new definition of truncation error is well suited to control and system identification problems where certain combinations of states, e. g., the system output, are of particular interest. We apply the new measures of accuracy to a recently developed model described in [1] and establish several associated properties which were previously unrecognized.