This paper proposes a technique for the logical labelling of document images. It makes use of a decision-tree based approach to learn and then recognise the logical elements of a page. A state-of-the-art OCR gives the physical features needed by the system. Each block of text is extracted during the layout analysis and raw physical features are collected and stored in the ALTO format. The data-mining method employed here is the \Improved CHi-squared Automatic Interaction Detection" (I-CHAID). The contribution of this work is the insertion of logical rules extracted from the logical layout knowledge to support the decision tree. Two setups have been tested; the rst uses one tree per logical element, the second one uses a single tree for all the logical elements we want to recognise. The main system, implemented in Java, coordinates the third-party tools (Omnipage for the OCR part, and SIPINA for the I-CHAID algorithm) using XML and XSL transforms. It was tested on around 1000 documents belonging to the ICPR'04 and ICPR'08 conference proceedings, representing about 16,000 blocks. The nal error rate for determining the logical labels (among 9 dierent ones) is less than 6%.