Ratings of familiarity and pronounceability were obtained for a sample of 199 names and 199 nouns. Frequency and familiarity were more closely related in the proper name pool than the word pool, although the correlation was modest in both cases. Familiarity and pronounceability were highly related for both names and nouns. Although word-level models of speech recognition have become very successful in the past several years due to the great increase in computer capacity and processing speed, even the most successful models generally require a great deal of specific training in order to reach high levels of recognition. For items such as proper names, which may number in the tens of thousands and have multiple pronunciations, it is impractical, if not impossible, to train on the entire set. Name recognition is of considerable practical interest given the possibilities of building acoustic interfaces to telephone directories or library catalogs, and shows promise as an area of great overlap between human and machine word recognition. Names occupy a unique position in lexical access: they have no inherent meaning thus they require phonological (sound-based) recognition; on the other hand, proper names have aspects of frequency and familiarity that may allow them to act like words already in the lexicon. A promising immediate approach to designing a name recognition system is to incorporate statistical aspects of proper names (frequency and familiarity) directly. There exists relatively little data on the distribution of proper names in the language (see however (l)), and there are a number of reasons to suppose that words and names will be rated differently on frequency and/or familiarity. We expect that those names that are familiar will also be easy to pronounce and, importantly for the computational aspect, ones that will lead to relatively little variability in pronunciation. Less familiar names may be more difficult to pronounce and result in more varied pronunciations. The data below are a first effort at obtaining reliable familiarity ratings for a random sample of surnames.