Preferential oxidation (PROX) of CO in excess hydrogen was investigated over cobalt catalysts supported on SrCO3, which showed a high performance. From the results of X-ray diffraction, X-ray photoelectron spectra (XPS), and temperature-programmed desorption, it was concluded that the active species for the PROX of CO is not cobalt oxide but cobalt carbonate-like compound, which was formed in the catalyst preparation step from Co(NO3)(2) precursor and SrCO3 support. On the basis of the multivariate analysis, characters by XPS analysis are the main factors to determine the CO conversion with Co/SrCO3 catalyst. The selectivity for CO oxidation was suppressed by the side reactions, such as H-2 oxidation and reverse water-gas shift reaction. Therefore, new additive to the Co/SrCO3 catalyst for the retardation of the side reactions was investigated by using an artificial neural network (ANN). The activities of 17 mol % Co + 1.7 mol % X/SrCO3 (X = B, K, Sc, Mn, Zn, Nb, Ag, Nd, Re and Tl) and 16 physicochemical properties of those 10 elements were used as training data of ANN, and the most optimal additive among 53 elements in periodic table was predicted by ANN. From the result of the prediction and experimental verification, boron addition to the catalyst was effective to increase the activity for CO oxidation at the reaction temperature range from 200 to 240 degrees C. Actually, 17 mol % Co + 5.1 mol % B/SrCO3 catalyst showed 99% CO conversion with 52% selectivity at 200 degrees C with a feed composition of 0.69 vol % CO, 0.69 vol % O-2, 4.5 vol % N-2, 10 vol % H-2 0, 17 Vol % CO2 and H-2 as a balance at a space velocity of 3 g . h/mol, and its activity was stable for 50 h.