This paper reports on the application of knowledge-based hybrid (KBH) modelling to an industrial scale (fed-) batch evaporative crystallisation process in cane sugar refining. First, principles models of the process lead in general to good description of process state, except for the prediction of the main crystal size distribution (CSD) parameters-mean size and the coefficient of variation. This is due to difficulties in expressing accurately nucleation and crystal growth rates and especially the complex phenomena of agglomeration in the relevant population balance. A hybrid model is proposed, which combines a partial mechanistic model that reflects the general mass, energy and population balances with a neural network to express growth rate, nucleation kinetics and agglomeration phenomena. Results obtained demonstrate a better agreement between experimental data and hybrid model predictions than that observed with the complete mechanistic model. (C) 2003 Elsevier Ltd. All rights reserved.