This article introduces PROC LCA, a new SAS procedure for conducting latent class analysis (LCA), multiple-group LCA, and LCA with covariates. LCA is a statistical method used to identify discrete, mutually exclusive latent classes of individuals based on their responses to observed categorical variables. The article provides an overview of LCA, including its application in various fields such as psychology, education, and sociology. It explains the estimation of parameters in traditional LCA, the use of covariates, and the handling of missing data. The article also discusses the importance of model specification, model assessment, and the interpretation of results. PROC LCA is demonstrated using data on alcohol use behavior in a national sample of high school seniors, exploring questions about underlying drinking behavior types, measurement invariance across sex, and the predictive power of grades and skipping school on latent class membership. The article concludes by highlighting the utility of PROC LCA for researchers and its potential for future extensions.This article introduces PROC LCA, a new SAS procedure for conducting latent class analysis (LCA), multiple-group LCA, and LCA with covariates. LCA is a statistical method used to identify discrete, mutually exclusive latent classes of individuals based on their responses to observed categorical variables. The article provides an overview of LCA, including its application in various fields such as psychology, education, and sociology. It explains the estimation of parameters in traditional LCA, the use of covariates, and the handling of missing data. The article also discusses the importance of model specification, model assessment, and the interpretation of results. PROC LCA is demonstrated using data on alcohol use behavior in a national sample of high school seniors, exploring questions about underlying drinking behavior types, measurement invariance across sex, and the predictive power of grades and skipping school on latent class membership. The article concludes by highlighting the utility of PROC LCA for researchers and its potential for future extensions.