The ability to calibrate optical proximity correction (OPC) models accurately and efficiently is desired to minimize the lithography process development time. To compare layout features used for lithography process model calibration, the concept of optical similarity is introduced that is derived from the optical intensity used in OPC models. The optical similarity analysis is based on comparing contributions to the overall intensity from the different optical kernels. Optical similarity is applied in comparing individual features as well as in the analysis of pattern coverage between sets of features used in calibration of models for OPC. A method for selecting features for calibration from a larger set of features is described. A systematic approach to apply relative weights to different calibration features in order to improve model fit on complex verification data is also presented. This systematic approach to feature comparisons and pattern coverage derived from optical properties is demonstrated on numerous examples from production lithography. The methods presented here can improve the feature selection process for model calibration to ensure pattern coverage relative to full chip layout and hence improve the overall OPC model quality.