Visualization is considered as a useful, complementary tool for exploring multi-dimensional data and in this way it can be considered as an alternative methods of data analysis. The benchmark visualization of the whole experimental space can help the researcher to reveal relationships, cross effects and trends in the whole system. Our method is the so called Holographic Mapping (HM), which provides integrated overview about a multi-dimensional space in a two-dimensional form. Before visualizing hyper surfaces quantitative relationships between input and output variables were established by means Artificial Neural Networks (ANNs). Nevertheless, ANNs are so called black boxes, which have to be combined with HM. Eventually, HM transforms sophisticated mathematical structure of ANNs into a human-comprehensible representation. Practically, HM enlightens the black box character of the ANNs, consequently the combination of HM with ANNs appears to be an excellent method for knowledge extraction. In this study visualization of results obtained in testing multi-component heterogeneous catalysts in various reactions, such as methane and propane oxidation, PROX reaction, methane oxidative coupling and water gas shift reaction will be presented allowing to find the cross effect between components and reaction parameters.