In this work, we investigate the problem of generating counterfactuals explanations that are both close to the data distribution, and to the distribution of the target class. Our objective is to obtain counterfactuals with likely values (i.e. realistic). We propose a method for generating realistic counterfactuals by using class prototypes. The novelty of this approach is that these class prototypes are obtained using a supervised auto-encoder. Then, we performed an empirical evaluation across several interpretability metrics, that shows competitive results with a state-of-the-art method.