The recurrent use of databases with categorical variables in different applications demands new alternatives to identify relevant patterns. Classification is an interesting approach for the recognition of this type of data. However, there are a few amount of methods for this purpose in the literature. Also, those techniques are specifically focused only on kernels, having accuracy problems and high computational cost. For this reason, we propose an identification approach for categorical variables using conventional classifiers (LDC-QDC-KNN-SVM) and different mapping techniques to increase the separability of classes. Specifically, we map the initial features (categorical attributes) to another space, using the Chi-square (C-S) as a measure of dissimilarity. Then, we employ the (t-SNE) for reducing dimensionality of data to two or three features, allowing a significant reduction of computational times in learning methods. We evaluate the performance of proposed approach in terms of accuracy for several experimental configurations and public categorical datasets downloaded from the UCI repository, and we compare with relevant state of the art methods. Results show that C-S mapping and t-SNE considerably diminish the computational times in recognitions tasks, while the accuracy is preserved. Also, when we apply only the C-S mapping to the datasets, the separability of classes is enhanced, thus, the performance of learning algorithms is clearly increased.