This study describes the design of a stemming algorithm for Wolaytta language. To give a solid background for the thesis, literatures on conflation in general and stemming algorithms in particular were reviewed. The result of the study is a prototype context sensitive iterative stemmer for Wolaytta language. Error counting technique was employed to evaluate the performance of this stemmer. The stemmer was trained on 3537 words (80% of the sample text) and the improved version reveals an accuracy of 90.6% on the training set. The number of over stemmed and understemmed words on the training set were 8.6% (304 words) and 0.8% (28 words) respectively. When the stemmer runs on the unseen sample of 884 words (20% of the sample text), it performed with an accuracy of 86.9%. The percentage of errors recorded as understemmed and overstemmed on this unseen (test set) were 9% and 4.1%, respectively. Moreover, a dictionary reduction of 38.92% was attained on the test set. The major sources of errors are also reported with possible recommendations to further improve the performance of the stemmer and also for further research.