The cross-classification method is an unsupervised analysis technique that extracts the ex- isting underlying structure between individuals and the variables in a data table as homoge- neous blocks. This technique is limited to variables of the same type, either numerical or categorical, and we propose to extend it by proposing a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by discretization in equal frequency in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a coclustering method between the individ- uals and the binary variables, leading to groups of individual and groups of variable parts. We apply this methodology on several data sets and compare with the results of a multiple correspondence analysis MCA applied to the same data.