The article presents the results of an exploratory study of the use of T.E.R.A., an automated tool measuring text complexity and readability based on the assessment of five text complexity parameters: narrativity, syntactic simplicity, word concreteness, referential cohesion and deep cohesion. Aimed at finding ways to utilize T.E.R.A. for selecting texts with specific parameters we selected eight academic texts with similar Flesch-Kincaid Grade levels and contrasted their complexity parameters scores to find how specific parameters correlate with each other. In this article we demonstrate the correlations between text narrativity and word concreteness, abstractness of the studied texts and Flesch -- Kincaid Grade Level. We also confirm that cohesion components do not correlate with Flesch --Kincaid Grade Level. The findings indicate that text parameters utilized in T.E.R.A. contribute to better prediction of text characteristics than traditional readability formulas. The correlations between the text complexity parameters values identified are viewed as beneficial for developing a comprehensive approach to selection of academic texts for a specific target audience.