Health care is one of the crucial components of basic social services that have a direct linkage to the growth and development of a country as well as to the welfare of the society. Ethiopian government has implemented different health policies such as disease prevention and control for neglected tropical disease, which aims to improve the health status of the community, particularly in the rural areas. To implement the program, the government needs many experts (health center staffs including extension workers). However, there are problems in implementing the automate program to provide information for the community and diagnosis the disease related to soil transmitted helminths infectious disease like they have not full knowledge on the implementation of the program, they are not be available on time to provide information for the community and diagnosis the disease related to soil transmitted helminths infectious disease in the country, they are not enough in number to reach to all rural areas, difficult for them to remember all health related information in mind. These problems collectively affect the health center coverage and performance, including Ethiopian health system. The objective of this study is to design an advisory expert system for Soil transmitted Helminths infectious diseases using one branch of artificial intelligence called expert system, specifically for Ascaris, whipworm and hookworm to reduce the above problems. This expert system is composed of knowledge base, inference engine and user interface. The user interface was designed using Java NetBeans IDE 8.2 with jdk1.8.0. The data was collected by interview from two nurses, three health extension workers in Bahir Dar health Center and rural areas and from different documents. The proposed system was evaluated by six health professionals using user acceptance testing by preparing questionnaire and four domain experts using system performance testing by preparing test cases. The developed expert system achieves 85.7% of user acceptance and 84.5% system performance results. The overall performance of the proposed system is 85.1%.