A part-of-speech (POS) tagger is a system that uses various sources of information to assign possibly unique POS to words. Automatic text tagging is an important component in higher level analysis of text corpora. Its output can also be used in many natural language processing applications. In languages like Turkish or Finnish, with agglutinative morphology, morphological disambiguation is a very crucial process in tagging as the structures of many lexical forms are morphologically ambiguous. This thesis present a POS tagger for Turkish text based on a full-scale two-level specification of Turkish morphology. The tagger is augmented with a multi-word and idiomatic construct recognizer, and most importantly morphological disambiguator based on local lexical neighborhood constraints, heuristics and limited amount of statistical information. The tagger also has additional functionality for statistics compilation and fine tuning of the morphological analyzer, such as logging erroneous morphological parses, commonly used roots, etc. Test results indicate that the tagger can tag about 97/% to 99% of the texts accurately with very minimal user intervention. Furthermore for sentences morphologically disambiguated with the tagger, an LFG parser developed for Turkish, on the average, generates 50% less ambiguous parses almost 2.5 times faster.