Hierarchical Classes (HICLAS) is a two-way, two-mode algorithm for modeling binary data into superset and subset categories (De Boeck & Rosenberg, 1988; Van Mechelen, De Boeck, & Rosenberg, 1995). This method has been used extensively in the study of personality, identity, social perception, autobiography, social networks, psychopathology, and clinical diagnosis (for reviews, see De Boeck, Rosenberg, & Van Mechelen, 1993, and Rosenberg, 1997). In this presentation I discuss the use of HICLAS in three settings. First, in studies on personal identity (Reich, Harber, & Siegel, in preparation; Reich, Mazzarella, Spence, & Siegel, in press), HICLAS was used to idiographically model (a) college students' and (b) first-time mothers' descriptions of self in several social roles. In both samples the interaction between two HICLAS-derived properties, self-congruence and negative elaboration, predicted scores on the Beck Depression Inventory. Second, HICLAS was used to model survey data from a civil court in California (Reich, Kressel, Scanlon, & Weiner, under review). HICLAS confirmed the existence of two hypothesized classes of items; litigants who answered "yes" to both sets of items were more likely to pursue mediation as an alternative to a court hearing. Third, HICLAS was used in a nonprofit agency that provides services to HIV+ clients seeking to form legal plans for the future care of their children (Reich & Hudis, in press). This analysis revealed that clients who used both psychotherapy and legal services classes that were clearly identified by HICLAS were more likely to complete a plan than clients using legal services alone. I will conclude with a demonstration of Individual Differences HICLAS (INDCLAS), a recently developed threeway threemode version of HICLAS, and open a dialogue on the possible uses of HICLAS in applied settings in India, e.g., education.