Machine-readable dictionaries have been regarded as a rich knowledge source from which various relations in lexical semantics can be effectively extracted. These semantic relations have been found useful for supporting a wide range of natural language processing tasks, from information retrieval to interpretation of noun sequences, and to resolution of prepositional phrase attachment. In this paper, we address issues related to problems in building a semantic hierarchy from machine-readable dictionaries: genus disambiguation, discovery of covert categories, and bilingual taxonomy. In addressing these issues, we will discuss the limiting factors in dictionary definitions and ways of eradicating these problems. We will also compare the taxonomy extracted in this way from a typical MRD and that of the WordNet. We argue that although the MRD-derived taxonomy is considerably flatter than the WordNet, it nevertheless provides a functional core for a variety of semantic relations and inferences which is vital in natural language processing.