A series of С/М x O y /SiO 2  nanocomposites has been synthesized through pyrolysis of a resorcinol-formaldehyde polymers pre-modified with metal oxide/silica nanocomposites. In turn, the binary nanoxides (M x O y /SiO 2 , where M = Mg, Mn, Ni, Cu and Zn) were sinthesized via thermal oxidative decompostion of metal acetates deposited onto fumed silica. These materials are promising for adsorption and concentration of trace amounts of organic substances or heavy metals. The nanocomposites exhibit mesoporosity and narrow size of pores, as seen from their distribution profiles. The porosity depends on composition of the materials. Hence, the textural parameters of carbon-containing and carbon-free types (or classes) served as input data to develop classification models of both materials classes using unsupervised method: hierarchical clustering. Once the cluster formula was derived, it was established that surface and the volume of micropores (Smicro, Vmicro) together with the volume of mesopores (Vmeso) have the the highest R2 (0.83 - 90) to enable successful clustering. Macroposity demonstrates the lowest fit (R2 < 0.1), and its two respective parameters (Smacro, Vmacro) are considered as the weakest contributors to the two-clusters model. In parallel, principal components analysis was helpful to distinguish the subject classes of the nanomaterials, at reduced number of the variables (three components at eigenvalue > 1). Two- and three-component K-Means clustering was conducted to assign each composite to its proper class. Thus, the case for two multivariate classes of nanomaterials can be described by varios independent methods of the data science.