This study provides relatively detailed information on developing Afaan Oromo morphologicalrnanalysis system. Morphological analyzer decomposes words into its components calledrnmorphemes and annotates those morphemes with grammatical information. Although the modulernuses machine-learning approach on morphological analysis, it used rule-based approach tornsegments words into its small components, morphemes. The developed prototype focused onrninflectional forms of nominals (nouns and adjectives) and verbs since the two words classes arernmostly the ones that undergo inflection, they determine the inflectional characteristics of thernlanguage. The protype was developed using python programming and Hidden Markov Modelrn(HMM). The Viterbi algorithm is used to encode the HMM model.rnThen, the prototype was trained and tested using representative data. A corpus of size 4,320rnnouns and 3,780 verbs are used to train the HMM model. Then the performance of the analyserrnwas tested using 480 nouns and 420 verbs.rnGenerally, the accuracy of the analyzer for nouns and verbs is 84.6 % and 82.9% respectively.rnThe result of the experiment was quite satisfactory, which can be improved by incorporatingrnsimple grammatical constraints and contextual information (including information encoded inrntonal system) to minimize the ambiguities, words root database to reduce errors duringrnmorphemes identification and additional data to emphasis the initial probability of the model.rnThe key limitations in this effort are limited funding opportunities, scarcity gold standard andrnbalanced annotated data sets and inherently multiple sources of ambiguity of the language atrndifferent levels.