Knowledge base systems exercise information technology to acquire and utilize combinedrnhuman expertise. The technology can be very useful to institutions with clear objectives,rnrules and problems to provide consistent answers for repetitive decision-making, processesrnand tasks.rnKnowledge base systems should be adopted and updated periodically to cater for the newrndiscoveries, and to enhance benefits by addressing the new changes in the clinical diagnosticrnactivities.rnThis research was done to preserve human expert level knowledge on the diagnosis of acuternrespiratory tract infections so that to make available such expert-knowledge for diagnosticrnactivities.rnThe system, also, could be useful especially in the medical environment where knowledgernexperts are few, often in scarcity and often soon retire before their expertise is documented.rnFacts that constituted the global criteria for the knowledgebase were gathered from expertrnphysicians, pharmacists and nurses at the hospital of Dagmawi-minilik and Meshualekiarnmiddle-level clinic, Addis Ababa, review of guidelines, manuals, journals of respiratoryrninfections, and online resources.rnThe system uses backward chaining with inference network and decision trees modelingrnstructures basing on facts to draw logical conclusions from the initial states to the final statesrnusing respiratory diagnostic functions.rnFor the prototype development, Prolog programming language has been used. Thernperformance of the prototype system is evaluated on qualitative bases. The result isrnencouraging to design a practical KBS for ARTI diagnosis.rnLastly, further studies should be done in artificial intelligence to solve the problem of rarernexpertise in the diagnosis of respiratory infections.