Node coloring is of main concern in Self-Organising Map (SOM) framework. 9,10 SOM is a non-linear dimensionality reduction method. It is designed to map items (generally from a high dimensional Euclidean space) onto a discrete grid: data are aggregated on each neuron (vector quantisation). Because the grid is discrete, the visualisation of gaps between clusters-if they exist-is uneasy. Various coloring methods have been proposed to account for this drawback, including. [11] [12] [13] [14] [15] [16] [17] In particular, Kaski et al 14, 15 display neurons in a CIELab colorimetric space; 16 Johan Himberg 17 uses a hierarchical classification in order to find clusters and use a cut-off function in the resulting tree so as to linking neurons to a LUT (thanks to a method close to the Fuas' one). 5, 6 However, the SOM context differs from the one of our present objective, despite approaches that are connected. Indeed, we focus here on taxonomies and SOM deals with multidimensional data. Moreover, if hierarchical classifications are often involved in automatic coloration of SOM, the tree is not the input of the algorithm, but a step in the method (especially in the context of cluster discovering). Such methods can be considered, however, as sources of inspiration for the present paper.