In this paper we present a combination of factorial projections and of SOM algorithm applied to a text mining problem. The corpus consists of 8 medieval texts which were used to teach arithmetic techniques to merchants. Classical Factorial Component Analysis (FCA) gives nice representations of the selected words in association with the texts, but the quality of the representation is poor in the center of the graphs and it is not easy to look for the successive projections to conclude. So using the nice properties of Kohonen maps, we can highlight the words which seems to play a special role in the vocabulary since they are associated with very different words from a map to another. Finally we show that combination of both representations is a powerful help to text analysis.