Latent class analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this article, a new methodology, multilevel latent class analysis (MLCA), is described and an applied example is presented. Latent classes of cigarette smoking among 10,772 European American females in 9th grade who live in 1 of 206 rural communities across the United States are considered. A parametric and nonparametric approach for estimating an MLCA are presented and both individual and contextual predictors of the smoking typologies are assessed. Both latent class and indicator-specific random effects models are explored. The best model was comprised of 3 Level 1 latent smoking classes (heavy smokers, moderate smokers, nonsmokers), 2 random effects to account for variation in the probability of Level 1 latent class membership across communities, and a random factor for the indicator-specific Level 2 variances. Several covariates at the individual and contextual level were useful in predicting latent classes of cigarette smoking as well as the individual indicators of the latent class model. This article will assist researchers in estimating similar models with their own data. (Contains 10 figures, 4 tables, and 1 footnote.)