This work presents the analysis of evaluation concerning the articles that are send to publication in academic journals, basing on additional parameters not resulting from essential value of the research work. Currently, majority of article verification algorithms is oriented on the selection of such works that are potentially more strongly influencing the international position of journal. For that purpose, editorial offices, and also reviewers, apply multi-criterion parametric evaluations and accepted parameters have often very subjective character. Presented work makes an attempt to identify used criterion functions i.e. defining evaluation parameters. These parameters were divided onto categories and there was proposed their preliminary verification basing on statistical analysis of already published articles in individual journals. Each parameter has attributed weight function, which allows to defined its impact on the total evaluation of article, and also adaptation of formula to any academic journal. Weight functions will be determined with usage of neural networks or genetic algorithms, aiming to their individual adaptation to particular journal.