  We describe a named entity tagging system that requires minimal linguistic
knowledge and can be applied to more target languages without substantial
changes. The system is based on the ideas of the Brill's tagger which makes it
really simple. Using supervised machine learning, we construct a series of
automatons (or transducers) in order to tag a given text. The final model is
composed entirely of automatons and it requires a lineal time for tagging. It
was tested with the Spanish data set provided in the CoNLL-$2002$ attaining an
overall $F_{\beta = 1}$ measure of $60\%.$ Also, we present an algorithm for
the construction of the final transducer used to encode all the learned
contextual rules.
