Fouling or encrustation is a significant problem in continuous crystallisation processes where crystal deposits at surfaces impede heat transfer, increase flow resistance and reduce product quality. This paper proposes an automatic algorithm to detect early stages of fouling using images of vessel surfaces from commodity cameras. Statistical analysis of the pixel intensity variation offers the ability to distinguish appearance of crystals in the bulk solution and on the crystalliser walls. This information is used to develop a fouling metric indicator and determine separately induction times for appearance of first crystals at the surfaces and in the bulk. A method to detect process state changes using Bayesian online change point detection is also proposed, where the first change point is used to determine induction time either at the surface or in the bulk, based on real-time online measurements without using any predetermined threshold which usually varies between experiments and depends on data acquisition equipment. This approach can be used for in situ monitoring of early signs of encrustation to allow early warning for corrective actions to be taken when operating continuous crystallisation processes. (C) 2015 Elsevier Ltd. All rights reserved.