The adsorption of polluted water is performed by activated carbon fibers. Three kinds of material are compared: microporous and mesoporous cloths and a microporous granular activated carbon. These porous solids are characterized by scanning electron microscopy and atomic force microscopy. BET surface areas and porous volumes are determined. Adsorption of a large number of organic compounds is carried out in water onto activated carbon cloths and granules. Kinetic and equilibrium data are expressed in terms of initial velocities and classical model parameters (Freundlich). These adsorbability parameters are then discussed according to solute molecular structures and activated carbon characteristics. The results obtained show that fluid-solid transfer is directly related to surface and porous structure (pore size distribution and pore connection with the external surface of adsorbents). The adsorption data of several aromatics and aliphatics onto a microporous activated carbon cloth are discussed statistically. A quantitative structure-property relationship method is used. Multiple linear regression and neural networks enable the assessment of correlations between the Freundlich adsorption parameter (log K) and molecular structure defined by molecular connectivity indexes. The neural network architecture is optimized, and results determined by the two statistical methods are compared: the neural network approach seems to give better results than multiple linear regression to assess this kind of relationship between adsorption and adsorbate molecular structure, even though its predictive ability is low. From a variable analysis, the results are discussed in terms of adsorbate positions at the adsorbent surface and mechanisms of interaction between solutes and an activated carbon surface are proposed.