The purpose of the work is to study the relationship of personality traits with the content of images posted in social networks. The paper attempts to identify in-formative features and appropriate ways to configure artificial neural networks. The developed technique includes obtaining several color-bright-based and statis-tical characteristics of image collections in the form of histograms and BoW dic-tionaries with further construction of classifiers based on artificial neural net-works to test the hypothesis about the interrelation between the available graphic data and the five-factor personality model of the tested. The questionnaire, which allowed the formation of training and test samples, was carried out by employees of the Psychological Institute of RAE with the “NEO-FFI” test, which included 60 questions. The collections of images used are datasets that published by users of the “VKontakte” social network. The problems of determining personality fac-tors were experimentally solved with using classifying and predictive artificial neural networks. The work confirmed the prevailing opinion that there is no sig-nificant interrelation (correlation) between placed images and “Big Five” personal factors. With the help of published images, the factors “Openness” and “Agreea-bleness” are predicted best, worst of all – “Neuroticism”. The results of forecast-ing personality recognition traits improve as the number of layers of neural net-works grows, up to overtraining moment.