Parts-of-Speech Tagger for Nepali Text using SVM is an application that assigns parts of speech like noun, pronoun, verb, adverb and other lexical tags to each word in Nepali text based on both its definition, as well as its context. The tagger is built using the Support Vector Machine learning framework that is trained with 80,000 lemmatized words from the Nepali National Monolingual Written Corpus. The average accuracy of 88% and 72% was obtained for lemmatized text and unprocessed raw text tagging system respectively.