Using loblolly pine, shortleaf pine, white oak, and northern red oak as examples, this paper considers the behavior of potential relative increment (PRI) models of optimal tree diameter growth under data uncertainity. Recommendations on intial sample size and the PRI iteractive curve fitting process are provided. Combining different state inventories prior to PRI model development increased sample size and diameter class representation while regionalizing the models. Differences arose between loblolly and shortleaf pine in both the contribution from each state inventory pool and the number of points used in the final model fitting. Generally, pooled models predicted the highest overall increment. Natural-origin loblolly pine produced a significantly different PRI model than planted loblolly. PRI curves for northern red oak in the Lake States and the Midsouth varied across the range of diameters considered, suggesting that widespread geographic differences in optimal performance may be present within a species. The PRI methodology is consistent with current theories on diameter growth and compares favorably with other model designs.