Modeling of complex processes often leads to complex mathematical relationships between inputs and outputs, which do not reflect the influence of the independent variables on the output parameters. In this article, an innovative technique based on neural networks is presented to extract fuzzy linguistic rules for modeling some processes using some input–output data. In this way, genetic algorithm is used both for optimal structure design of those group method of data handling-type neural networks and for subsequent optimization of sub-bounds of fuzzy singleton antecedents to further optimize the obtained fuzzy rule base. Three different input–output data tables related to some complex problems of a nonlinear mathematical system, an explosive cutting process and the probability of failure estimation of a two mass-spring system are modeled by some fuzzy rules, using the technique discussed in this article.