Most printed Chinese words are compounds built from the combination of meaningful characters. Yet, there is a poor understanding of how individual characters contribute to the recognition of compounds. Using a megastudy of Chinese word recognition (Tse et al., 2017), we examined how the lexical decision of existing and novel Chinese compounds was influenced by two properties of individual characters: family size (the number of distinct words that embed a character) and family semantic consistency (the average semantic relatedness between a character and all words containing it). Results revealed that both variables influence word and nonword processing: Words are recognized more quickly and accurately when they contain characters that occur frequently across different words and that make consistent meaningful contributions to those words, while nonwords containing those types of characters are rejected more slowly. These findings suggest that the learning of individual characters is based not only on the quantity of experience with them but also on the reliability of the semantic information they communicate. In addition, readers are able to generalize character knowledge acquired from previous word experiences to their daily encounters with familiar and unfamiliar words. We close by discussing how word experience shapes character knowledge when different ways of calculating family properties are considered.