A compact variation-aware timing model for a standard cell in a cell library is developed using Principal Component Analysis (PCA) and experimental design. The model incorporates variations in the input waveform and loading, process parameters, and the environment into the cell timing model. PCA is used to form a compact model of a set of waveforms impacted by these sources of variation. Cell characterization involves describing how waveforms are transformed by a cell as a function of the input waveforms, process parameters, and the environment. The models have been evaluated by calculating the delay of paths. The results demonstrate improved accuracy in comparison with table-based static timing analysis at comparable computational cost. Complexity and accuracy of the models are also discussed.