Abstract This paper revisits probabilistic, model–based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model–based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via Deep Learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyse how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates