Rice plant diseases are all diseases that occur in rice plants. One of the factors that causes rice productivity to decrease is disease that attacks rice plants. This research aims to design a web-based expert system for handling rice plant diseases using the Case Based Reasoning (CBR) method. The CBR method solves new problems by referring to solutions from previous cases through four main steps: retrieve, reuse, revise, and retain. Data was collected through interviews with experts and literature studies, then analyzed using CBR. The design process involves the use of PHP programming, while system testing is carried out to ensure compliance with the design. This system allows users to diagnose rice plant diseases based on input symptoms, such as Brown Planthoppers, Stem Mover Pests, Blast, and Sheath Blight, with a high level of accuracy. With this system, it is hoped that farmers can handle diseases in rice plants more efficiently, thereby increasing productivity and reducing losses. This system was built with 4 diseases with 18 symptoms that have different weights determined by experts. Case-Based Reasoning was developed from a similarity-based learning system. Based on Case-Based Reasoning method calculations, the similarity to brown planthopper disease was 0.84, stem mover was 0.85, blast was 0.46 and sheath blight was 0.85.