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Training course recommendation is one of the important steps in training planning for SMEs. In this process, an expert analyzes SME's current and desired status, and recommends training courses that can help to improve its productivity. However this process has 3 drawbacks; first is the access problem. It's because SMEs often don't have the possibility to employ permanent training experts so they have difficulties in reaching experts. Second is the loss of knowledge problem. It's because most SMEs are not concern about issues like accumulating knowledge in organizations. So even if SME employs an expert for a while, as the expert leaves the company his knowledge is gone too. And third is the locality problem, which is because SMEs often have a very little relation with other successful SMEs and their decisions are limited to their own experiences. To refine these drawbacks, this paper proposed a training course recommender system based on the hybridization of Case-based reasoning and Fuzzy logic. CBR methodology uses the past experiences to solve new similar problems. FL on the other hand helps to cope with the uncertain and imprecise characteristics of SMEs. It is used to enhance case representation and case retrieval in CBR cycle. FCRS, as a computer application can solve the access problem. Each incubator can provide one for its SMEs. It can solve the loss of knowledge problem by accumulating expert's knowledge in the form of the past experiences and reuse them to design a new Training plan. It also can solve the locality problem by recommending successful training plans that other similar SMEs has taken.