nformation is very important in our day to day activity. Technology plays an important role in order to satisfy human beings information need through the use of Internet where people ask questions and a system provides an answer for their query. For instance, search engines a user submit a query and the search engine displays a link to relevant web pages for each issued users query. The QA systems emerge as best solution to get the required information to the user with the help of information extraction techniques. QAS has been developed for English, Amharic, Afaan Oromo and other languages. The Afaan Oromo QAS is developed for answering factoid type questions where the answer is named entity. In this thesis, QAS is developed for answering list, definition and description question which deals with more complex information need. Document preprocessing, question analysis, document selection and answer extraction are the components used for developing the QAS. Tokenization, case normalization, short word expansion, stop word removal, stemming, lemmatization and indexing are the tasks of pre-processing. Question classification is done using a rule based approach. The subcomponents in document selection are document retrieval used for retrieving relevant documents and document analysis used for filtering the retrieved documents. The answer extraction component have sentence tokenizer for tokenizing sentences retrieved from the document analysis and independent subcomponents for definition-description and list were used, DDAE contains sentence extractor for extracting sentences from sentence tokenizer, the answer selection algorithm selects top 6 sentences from the scored and ranked sentences and finally sentence ordering algorithm order the sentences. The LAE contain candidate answer extraction for extracting through rules and gazetters and select answer. The system is tested using evaluation metrics. We used percentage ratio for evaluating question classification which classified 98% correctly. The performance of document selection and answer extraction is tested using precision, recall and F- score. Document selection component is tested and scored an F-score of 0.767. Finally, the answer extraction component is evaluated with an average F-score of 0.653. Keywords: Afaan Oromo List, Definitional and Descriptional Question Answering, Rule Based Question Classification, Document Filtering, Sentence Extraction, Answer Selection