We address the problem of morpho-syntactic disambiguation of arbitrary texts in a highly inflectional natural language. We use a large tagset (615 tags), EAGLES and MULTEXT compliant [5]. The large tagset is internally mapped onto a reduced one (82 tags), serving statistical disambiguation, and a text disambiguated in terms of this tagset is subsequently subject to a recovery process of all the information left out from the large tagset. This two step process is called tiered tagging. To further improve the tagging accuracy we use a combined language models classifier, a procedure that interpolates the results of tagging the same text with several register-specific language models.