This paper presents a study about methods for normalization of historical texts. The aim of these methods
is learning relations between historical and contemporary word forms. We have compiled training and test
corpora for different languages and scenarios, and we have tried to read the results related to the features
of the corpora and languages. Our proposed method, based on weighted finite-state transducers, is com-
pared to previously published ones. Our method learns to map phonological changes using a noisy channel
