This paper presents the Latent Class Level-PCM as a method for identifying and interpreting latent classes of respondents according to empirically estimated performance levels. The model, which combines elements from latent class models and reparameterized partial credit models for polytomous data, can simultaneously (a) identify empirical boundaries between performance levels and (b) estimate an empirical location of the centroid of each level. This provides more detailed information for establishing performance levels and interpreting student performance in the context of these levels. The paper demonstrates the use of the Latent Class L-PCM on an assessment of student reading proficiency for which there are strong ties between the hypothesized theoretical levels and the polytomously scored assessment data. Graphical methods for evaluating the estimated levels are illustrated.