In this work, we developed a novel method to generate comparison trees based on characteristics collected from metabolic networks of bacteria. We characterize each bacterial genome’s metabolism by the occurrence frequencies of various chemical reactions classified by enzyme commission numbers, and by the correlation of the reaction types for any two consecutive reactions in pathways present in the networks. In hypothesizing that species physiologically close to each other should show high similarity in these characteristics, we quantitatively measure the similarity using Pearson correlation coefficient, and build comparison trees using the Neighbor-Joining algorithm. These Metabolic Characteristics (MC) based comparison trees cluster the bacteria according to their functional groups and reveal the relationship between different organisms from a physiological perspective yielding new insights about the organisms.