One of the problems in part-of-speech tagging of real-word texts is that of unknown to the lexicon words. In (Mikheev, 1996), a technique for fully unsupervised statistical acquisition of rules which guess possible parts-ofspeech for unknown words was proposed. One of the over-simplification assumed by this learning technique was the acquisition of morphological rules which obey only simple coneatenative regularities of the main word with an affix. In this paper we extend this technique to the nonconcatenative cases of suffixation and assess the gain in the performance.