Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. Many approches have been studied and have shown their capacity to extract such structures in continuous, binary or contingency tables. However, very little work has been done to perform co-clustering on mixed type data. In this article, we extend the use of latent bloc models to co-clustering in the case of mixed data (continuous and binary variables). We then evaluate the effectiveness of our extention on simulated data and we discuss its potential limits.