With the adoption of statistical timing across industry, there is a need to characterize all gates/cells in a digital library for delay variation (referred to as statistical characterization). Statistical characterization needs to be performed efficiently with acceptable accuracy as a function of several process and environmental parameter variations. In this paper, we propose an approach to consider intra-cell process mismatch variations to characterize a cell's delay and output transition time (output slew) variations. A straightforward approach to address this problem is to model these mismatch variations by characterizing for each device fluctuation separately. However, the runtime complexity for such characterization becomes of the order of number of devices in the cell and the number of simulations required can easily become infeasible. We analyze the fluctuations in switching and nonswitching devices and their impact on delay variations. Using these properties of the devices, we propose a clustering approach to characterize for cell's delay variations due to intra-cell mismatch variations. The proposed approach results in as much as 12X runtime improvement with acceptable accuracy, compared with Monte Carlo simulation. We show that this approach ensures an upper bound on the results while keeping the number of simulations for each cell independent of the number of devices.