Understanding a text in order to learn is subject to modeling and is partly dependent to the complexity of the read text. We transpose the evaluation process of textual complexity into measurable factors, identify linearly independent variables and combine multiple perspectives to obtain a holistic approach, addressing lexical, syntactic and semantic levels of textual analysis. Also, the proposed evaluation model combines statistical factors and traditional readability metrics with information theory, specific information retrieval techniques, probabilistic parsers, Latent Semantic Analysis and Support Vector Machines for best-matching all components of the analysis. First results show a promising overall precision (>50%) and near precision (>85%).