One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other subsets. In the case of community college students, for example, clusters may be formed on the basis of student aspirations, student course-taking and enrollment behaviors, student demographic characteristics, or any combination of these or other measures. These clusters assist in the identification of patterns of behaviors or characteristics in data sets that contain an otherwise incomprehensible amount of student information. In this article, the authors provide an introduction to the cluster analytic method as it pertains to research on community college students. The execution of cluster analysis requires a number of carefully considered methodological decisions. The first set of decisions concerns the selection of the variables that will be included in the analysis, the scaling of these variables, and the structure of the data. The second involves the selection of a proximity metric by which within-group homogeneity and between-group heterogeneity will be calculated. Then, the cluster technique itself must be selected. Finally, one must decide how to make sense of the identified clusters. The authors describe each stage of the decision-making process and detail several common approaches at each stage. They cite many comprehensive sources throughout to aid researchers in locating additional informational resources.