The aim of this work was to extract knowledge for dry reforming of methane (DRM) reaction from experimental data using data mining tools such as decision trees and artificial neural networks. An extensive database containing 5521 data points depending on 63 catalyst preparation and operational variables was constructed from 101 papers published between 2005 and 2014; the output variables were CH₄ conversion, CO₂ conversion and H₂/CO ratio of the product stream. Then, the database, as a whole or as subsets for different base metals were analyzed using decision trees (DT) to develop heuristics for high performance and artificial neural networks (ANN) to determine relative importance of input variables and predict the performance under unstudied conditions; mostly CH₄ conversion, which is the most frequently reported output variable, were used in analysis. The testing accuracy of the decision tree was about 80% leading to four heuristics (i.e. four possible courses of action) for high CH₄ conversion over Ni based catalyst. The first decision point to separate these heuristics is the reaction temperature as can be expected. This is followed by the other variables such as support type, W/F and reduction temperature. ANN analysis revealed that operational variables have higher relative importance (55%) compared to catalyst preparation variables (45%). The most important operational variable was found to be the reaction temperature while the active metal and the support are the most important catalyst preparation variables. ANN model was also tested to predict the data, which was not seen by the model before, and the data in 65 papers out of 101 were predicted within 15% error while 76 papers had the error rate of less than 20%.