The aim of this research is to design a prototype case based recommender system for touristrnattraction area and visiting time selection that can assist experts and tourists to make timelyrndecisions. For the development of case based recommender system, essential knowledge wasrnacquired through semi-structured interview and document analysis. Eight domain experts andrnfourteen visitors were interviewed to elicit the required knowledge about the selection processrnof attraction area. The acquired knowledge was modeled using hierarchical tree structure andrnit was represented using feature value case representation. At the end, jCOLIBRIrnprogramming tool was used to implement the system.rnThe main data source (case base) used to develop case based recommender system for touristrnattraction area selection is previous tourist cases collected from NTO and MoCT. As a retrievalrnalgorithm, nearest neighbor retrieval algorithm is used to measure the similarity of new casern(query) with cases in the case base. Accordingly, if there is a similarity between the new casernand the existing case, the system assigns the solution (recommended attraction area andrnvisiting time) of previous case as a solution to new case.rnTo decide the applicability of the prototype system in the domain area, the system has beenrnevaluated by involving domain experts and visitors through visual interaction using therncriteria of easiness to use, time efficiency, applicability in the domain area and providingrncorrect recommendation. Based on prototype user acceptance testing, the average performancernof the system is 80% and 82% by domain experts and visitors respectively. The performance ofrnthe system is also measured using the standard measure of relevance (IR system) recall,rnprecision and accuracy measures, where the system registers 83% recall, 61% precision andrn85.4% accuracy. Finally, conclusion and future research directions are forwarded.