This talk presents possible strategies for the calibration of crystallization models aimed at predicting particle size distributions (PSDs) of active pharmaceutical ingredients (APIs) when using industrial datasets, which are limited in terms of number or information for the modeling exercise. Industrial data concerning a seeded batch cooling recrystallization of an API in an organic solvent are used as a case study, representing an example of the issues to be faced with real-world experimental datasets. The results are discussed showing how the model performances can be deemed satisfactory, at least from the industrial perspective, and how this can be useful to enhance process understanding and to guide process development and scale-up.