This research explores case-based reasoning for robotic assembly cell diagnosis. The case-based reasoning approach to cell diagnosis is different from the case-based reasoning approach to general diagnosis problems which have enough past cases or examples. Since the failure cases of a robotic assembly cell are not generally available in the early stage of cell operation, the cell failures should be artificially generated from the design information of cell struc-ture and assembly sequence; the analysis of generated cell failures are then used for the cell diagnosis. The case representation, case management and search for appropriate case from case database are studied with an example of a robotic assembly cell. The failure cases are hierarchically distributed and multiply indexed within the hierarchical causal model of the robotic assembly cell. The diagnostic performance gradually increases as the diagnostic case data-base grows.