A word is treated as ‘ambiguous’ if it has more than one interpretation. Deciphering the meaning of an ambiguous word as per its context is known as word sense disambiguation (WSD). WSD, an open problem in natural language processing (NLP), has significant implication on various NLP applications, hence needs proper attention. Many efficient and robust approaches exist to tackle WSD issue and have been explored extensively for different languages. In this research work, the first attempt is made to analyze this problem in Kashmiri language. For this, we used two popular machine learning algorithms, k-NN and decision tree, and contrasted their performance. For experimentation, a novel sense tagged corpus is created for fifty commonly used and highly ambiguous Kashmiri words. The instances for the selected ambiguous words are extracted from a raw Kashmiri corpus of 500 K tokens. To tag the selected ambiguous terms with the appropriate sense, Kashmiri WordNet is used. Decision tree and k-NN-based classifiers are trained based on contextual features extracted from the sense tagged corpus. Both the algorithms are tested on all fifty target words. Accuracy, precision, recall and F1-measure are calculated for both the algorithms and compared. Although both the algorithms performed well, the decision tree-based classifier showed lower performance than the k-NN-based classifier in many cases.