Case-Based Reasoning is used when generalized knowledge is lacking. The method works on a set of cases formerly processed and stored in the case base. A new case is interpreted based on its similarity to cases in the case base. The closest case with its associated result is selected and presented as output of the system. Recently, Dissimilarity-based Classification has been introduced due to the curse of dimensionality of feature spaces and the problem arising when trying to make image features explicitly. The approach classifies samples based on their dissimilarity value to all training samples. In this paper, we are reviewing the basic properties of these two approaches. We show the similarity of Dissimilarity based Classification to Case-Based Reasoning. Finally, we conclude that Dissimilarity based Classification is a variant of Case-Based Reasoning and that most of the open problems in Dissimilarity-based Classification are research topics of Case-Based Reasoning.