Studies conducted so far and annual reports frequently issued by the national bank ofrnEthiopia indicated that agriculture remained the main source of living for the greatest portionrnof Ethiopia's population. Despite the fact that government is giving attention to the sector,rnmost farmers are still using their traditional knowledge to solve crop related problems. Thernvery limited number of specialized experts in the area, coupled with lack of appropriaterntechnologies, contributed to the present low productive land and low income status ofrnEthiopian farmers. Maximizing yield potential and quality need the presence of proper expertrndecisions at a field level. In situations where there is a shortage of high level domainrnexperts, automating crop management decision making has paramount importance. Expertrnsystem technologies can be used for automating crop management decisions as they havernbeen effectively applied to solve problems in other domain areas of similar nature.rn Based on the information gathered at the start of this research, the problem of the country'srnagricultural human resource is more intense in the area of vegetable production. With thisrnbackground, this research is conducted to develop an expert system model as an attempt tornautomate the reasoning strategy of human vegetable experts. There are a number ofrnapproaches to develop expert systems ranging from rule based methods that representrnknowledge in the form of IF-THEN rules to systems that employ machine learningrntechniques.rnThe approach adopted in this research uses the combination of the rule based and neuralrnnetwork methods with an aim to exploit the best features of the two methods. The system isrnmodeled to have hybrid architecture by integrating rule based and neural network modulesrnas a component of one single system.rnIn the course of building the hybrid model, knowledge acquisition, data preprocessing, rulerngeneration, knowledge representation and model integration tasks had been performed. Inrnthe rule based module of the hybrid model, knowledge of vegetable experts was representedrnas rules. To build the neural network module and perform the integration with the rule basedrnmodule, the fast artificial neural network libraries written in the C language were used afterrncompiling and importing them in the prolog environment. The neural network module is builtrnto handle user requests that may go beyond the capability of the rule based module. Thernartificial neural network module was integrated with the rule based module to create thernhybrid vegetable expert system.rnTo measure the effect on performance after integration, ten random queries of consultationrnrequests were presented to both the rule based module and the hybrid system. The hybridrnsystem responded to eight of them while the rule based module alone provided answers tornonly five of these questions. The performance gain observed in the hybrid system is due tornthe neural network module embedded in it. The result obtained in this work showed thatrnintegration of the two approaches into one system produced better result and it isrnencouraging to advance the system into fully functional vegetable expert system.