Classifying species into functional groups is a way to understand the functioning of species-rich ecosystems, or to model the dynamics of such ecosystems. Many statistical techniques have been defined to classify species into groups, and a question is whether different techniques bring consistent classifications. In a tropical rain forest in French Guiana, five species classifications have been defined by different authors for the purpose of forest growth modelling but using different data sets and different statistical techniques. The correspondence between the five classifications was measured using four indices that are generalizations of existing indices to compare two classifications. A multiple correspondence analysis was used to identify associations between groups of different classifications. In a second step, two-table multivariate analyses were used to characterize the relationships between species classifications and eight species traits (consisting of seven populational traits and one functional trait). We evidenced a consensus on the potential size of trees: species were similarly clustered by the five classifications along this trait that is correlated to turnover rate. More surprisingly, no consensus was found for growth rate, nor wood density, traits that are correlated with light requirement.