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The use of natural language rules that are able to handle vague and, possibly, even contradicting knowledge in order to model formal dependences is an intriguing idea. Fuzzy if-then rules have been proposed as classification methods that can easily be defined and interpreted by humans or built automatically by learning algorithms. This paper gives an intuitive insight into the properties and the behavior of prototype-based fuzzy classifiers, using formal descriptions and visualization methods. This can help to avoid some common peculiarities and pitfalls in the manual or automated design of fuzzy classifiers.