This paper discusses the use of the bayesian network model for a classification problem related to the document image understanding field. Our application is focused on logical labeling in documents, which consists in assigning logical labels to text blocks. The objective is to map a set of logical tags, composing the document logical structure, to the physical text components. We build a bayesian network model that allows this mapping using supervised learning, and without imposing a priori constraints on the document structure. The learning strategy is based partly on genetic programming tools. A prototype has been implemented, and tested on tables of contents found in periodicals and magazines.