Sea state is a subjective quantity whose accuracy depends on an observer’s ability to translate local wind waves into numerical scales. It provides an analytical tool for estimating the impact of the sea on data quality and operational safety. Tasks dependent on the characteristics of local sea surface conditions often require accurate and immediate assessment. An attempt to automate sea state classification using eleven years of ship motion and sea state observation data is made using parametric modeling of distribution-based confidence and tolerance intervals and a probabilistic model using sea state frequencies. Models utilizing distribution intervals are not able to exactly convert ship motion data into various sea states scales with significant accuracy. Model averages compared to sea state tolerances do provide improved statistical accuracy but the results are limited to trend assessment. The probabilistic model provides better prediction potential than interval-based models, but is spatially and temporally dependent.