Using tools of combinatorial catalysis and high-throughput experimentation techniques new multi-component catalysts have been designed and tested for total oxidation of methane. The compositions of catalysts were optimized by means of holographic research strategy (HRS). In the first catalyst generation, the best hit resulted in 44% conversion of methane. However, after designing and testing 167 compositions in five generations, the best catalysts resulted in practically complete conversion of methane at 350° C. The supports of the best catalysts found by HRS consist of mostly Cc oxide and small amount of La. In the best catalyst, the concentration of Pt, Pd and An is 2.3, 2.3 and 0.1%, respectively. In order to obtain the catalytic activity versus composition relationship, artificial neural networks (ANNs) have been trained using catalytic results of the HRS optimization. Upon combining HRS and ANNs, "virtual" catalytic experiments were performed in order to (i) find "virtual" optimum compositions and (ii) map the full experimental space in two dimensions. Results obtained in this study proved that HRS is a very powerful tool both in catalyst library design and visualization of the experimental space. The combination of HRS with ANNs appeared to be an excellent method for knowledge extraction. In this way, further new information can be obtained about the catalytic system investigated. © 2005 Elsevier B.V. All rights reserved.