The corporate governance quality has always been a decision criterion for investments, many recent studies trying to define metrics in order to help investors in their decision process. In this paper we investigate whether the clustering of companies’ information concerning their corporate governance politics and financial information could be mapped with the help of clustering. Our approach is to build clusters using machine learning techniques, based on corporate governance and financial variables from a number of 1400 listed companies. We evaluate the obtained clusters by matching them with the classes of two well-known indicators (Tobin’s Q and Altman Z-score), used to estimate the companies’ performance. We obtain partial matches of the benchmark variables and we compare the performances of the used algorithms.