Abstract: Natural Language Processing (NLP) is an area which is concerned with the computational aspects of the human language. The main difficulty in natural language processing tasks is perhaps its ambiguity. Ambiguity in natural language pervades virtually all aspects of language analysis. Sentence analysis in particular exhibits a large number of ambiguities that demand adequate resolution before the sentence can be understood. Most of the language processing applications like Information Retrieval (IR), Information Extraction (IE), Question-Answering systems, Text Summarization and Machine Translation (MT) are affected by the highly ambiguous nature of natural language. Ambiguities in sentence analysis are generally categorized into two types: lexical and structural ambiguities. The present work describes the methodology to resolve lexical ambiguity in a Kannada sentence. Lexical ambiguity arises when a lexical item has alternate meanings and different Part-Of-Speech (POS) tags. The paper describes the decision list based algorithm to disambiguate Kannada polysemous words. We built Kannada corpora using web resources. It is further divided in to training and testing corpora. The decision list required for disambiguation task is created using training corpora. The example sentences needs to be disambiguated are stored in testing corpora. The proposed algorithm attempts to disambiguate all the content words such as nouns, verbs, adverbs, adjectives in an unrestricted Kannada text sentence. The algorithm is based on one powerful assumption that words tend to have one sense per collocation. i.e. the nearby words provide strong and consistent clues to the sense of the ambiguous word.