The main goal of our study is the analysis of data obtained from molecular modeling for a series of imidazole derivatives that possess strong antifungal activity. The research was designed to use artificial neural network (ANN) analysis to determine quantitative relationships between the structural parameters and anti-Streptococcus pyogenes activity of a series of imidazole derivatives. ANN in association with quantitative structure-activity relationships (QSAR) represents a promising tool in the search for drug candidates among the practically unlimited number of possible derivatives. In this work, a series of 286 imidazole derivatives presented as cationic three-dimensional structures was used. The activity was expressed as a logarithm of the reciprocal of the minimal inhibitory concentrations, log 1/MIC. Multilayer perceptron ANN was used for predictions of antimicrobial potency of new imidazole derivatives on the basis of their structural descriptors. The obtained correlation coefficient equaled 0.9461 for the learning set, 0.9060 for the validation set and 0.8824 for the testing set of imidazole derivatives. Hence, satisfactory and practically useful predictions of anti-Streptococcus pyogenes activity for a series of imidazole derivatives was obtained, supporting the future successful interpretation of QSAR analysis for those compounds.