Often, the classification of words does not go beyond "difficult" (i.e., infrequent, late-learned, nonimageable, etc.) or "easy" (i.e., frequent, early-learned, imageable, etc.) words. In the present study, we used a latent cluster analysis to divide 703 Dutch words with scores for eight word properties into seven clusters of words. Each cluster represents a group of words that share a particular configuration of word properties. This model was empirically validated with three data sets from Grades 2 to 4 children who made either a lexical decision task or a use decision task with a selection of the words. Significant differences were found between the clusters of words within the three data sets. Implications for further study and for practice are discussed.