This work evaluates the efficiency of different word classes -part of speech-, normalized vs. non normalized counting for syllable and word occurrences, to predict non orthographic breaks of an Argentine Spanish database, designed for the development of the prosody component for a Text To Speech system. Within a set of 741 sentences, regression trees were trained and tested with two different proportions of data. The results show an error range of 8 to 15% whose minimum value is related to a reduced amount of morphologic categories, and a normalized counting of syllables and words.