Machine Learning is an essential task for any recognition problem, since it permits to train the intelligent system from examples. The structuration of this knowledge as symbolic rules is the domain of grammatical inference, discipline in which the group has been specially active.

In this area of machine learning, it has been designed several algorithms that build correct models (finite state automata, stochastic automata, tree grammars, depending on the case) from examples of a language (which in each case they will be strings of symbols, trees, etc). Also, it has been developed methods that learn to translate from examples and permit also to use information about the characteristics of the language in order to accelerate the learning process.