This paper presents an original experiment aimed at evaluating if state-of-the-art visual clustering techniques are able to automatically recover morphological classifications built by the botanists themselves. The clustering phase is based on a recent Shared-Nearest Neighbours (SNN) clustering algorithm, which allows to combine effectively heterogeneous visual information sources at the category level. Each resulting cluster is associated with an optimal selection of visual similarities, allowing to discover diverse and meaningful morphological categories even if we use a blind set of visual sources as input. Experiments are performed on ImageCLEF 2011 plant identification dataset, that was specifically enriched in this work with morphological attributes tags (annotated by expert botanists). The results are very promising, since all clusters discovered automatically can be easily matched to one node of the morphological tree built by the botanists.