Previously many sentence parsers are developed for foreign languages such as English, Arabic, etc. as well as for Amharic language from local languages of Ethiopia. Parsing Afan Oromo sentence is also needed and a necessary mechanism for other natural language processing applications like machine translation, question answering, knowledge extraction and information retrieval. Thus, we have been developed rulebased parser using a top-down chart parsing algorithm for Afan Oromo sentences, which include both simple and complex sentences. Context Free Grammar (CFGs) was used to represent the grammar of the language. 500 sentences for sample corpus were prepared and CFG was extracted manually from sample tagged corpus. We also developed simple algorithm of a lexicon generator to automatically generate the lexical rules. Python programming language and NLTK are used as an implementation tools for this study. Then, the experimentation took place on a parser. The parser was trained on 400 sentences of training dataset with the accuracy of 98.25% and tested on 100 sentences of testing dataset with the accuracy of 91%. Thus, the experimental results on a parser is an encouraging result since it is the first work for simple and complex sentences of Afan Oromo language. Finally, we have been reported that the conclusion and possible recommendation for future work in the last chapter.