This paper presents a part-of-speech tagging system for the Uzbek language based on the Conditional Random Fields (CRF) approach. Using a manually annotated corpus and a set of language-specific morphological and contextual features, the model was trained and evaluated through 5-fold crossvalidation. The experiments showed promising results with an average F1-score of approximately 89%, confirming the effectiveness of the CRF-based method for handling the morphological complexity of Uzbek. This work contributes to the development of NLP tools for low-resource languages like Uzbek.