In hot strip mills, estimation of rolling variables is of crucial importance to setting up the finishing mill and meeting dimensional control requirements. although the use of physics-based models is preferred by the specialists to keep the fundamental knowledge of the underlying phenomena, many times a purely empirical model, such as an artificial neural network, will provide better predictions although at the cost of losing such fundamental knowledge. This paper presents the application of physics-based and artificial neural networks-based hybrid models for scale breaker entry temperature prediction in a real hot strip mill. The idea behind combining these two types of models is to capitalize in what are often portrayed as their main advantages: (i) keeping the physics knowledge of the process and (ii) providing better predictions. Temperature prediction schemes with different hybrid levels between a pure heat transfer model and an artificial neural network alone were evaluated and compared showing promising results in this case study. Using an artificial neural network together with the heat transfer model helped to achieve better temperature predictions than using the heat transfer model alone in every instance, thereby proving the hybrid schemes attractive to the industry. In this work, three different hybrid schemes combining the knowledge imbedded in a heat transfer model and the prediction capabilities of an artificial neural network in temperature prediction in a hot strip mill were tried. The hybrid models came out quite competitive in this case study. The results support the use of empirical models to foster the prediction ability of physics-based models; that is, they make the case for their joint use as opposed to their exclusive use.