The applicability of the CHR (clustering hybrid regression) approach was evaluated in modeling the H-2 production rate from the metabolic endproducts (ethanol, acetate, butyrate, propionate, and valerate), CO2 production rate, and monitoring variables (pH, oxidation-reduction potential, and alkalinity) of the bioreactor system. Self-organizing maps (SOMs) were used to visualize and understand the relationships between the variables in the multidimensional data set. K-means clustering was used to cluster the data set into statistically significant clusters. The local multiple-regression models for modeling the H-2 production rate were formulated for each cluster. The data was obtained from the xylose (concentration 20 gCOD/L) based fermentative H-2-producing continuously stirred tank reactor (CSTR). The bioreactor (working volume, 4 L) was operated for 376 days at 35 +/- 1 degrees C and a hydraulic retention time of 12 h. The data was obtained when the bioreactor reached steady-state conditions. Different metabolic patterns (acetate and butyrate as the main metabolic products) in anaerobic xylose degradation were investigated. High H-2 production rates were observed during two states: first, when butyrate metabolism at pH 7 was occurring; and second, when acetate coupled metabolism at pH 6.7 was taking place in the bioreactor.