The authors maintain that the essential feature that characterizes a machine translation approach and sets it apart from other approaches is the kind of knowledge it uses. From this perspective, the authors argue that example-based machine translation is sometimes characterized in terms of non-essential features. The authors show that example-based machine translation, as long as it is linguistically principled, significantly overlaps with other linguistically principled approaches to machine translation. The paper makes a proposal for translation knowledge bases that make such an overlap explicit. The authors relate their proposal to translation by analogy, which stands out as an inherently example-based technique.