This study used latent class analysis (LCA) with binary observed indicators to identify latent classes of victimization, based on the extent to which adolescents in the U.S. experienced traditional victimization and cyber-victimization. Data were collected by the National Center for Education Statistics and the Bureau of Justice Statistics using 2013 School Crime Supplement of the National Crime Victimization Survey. The sample included 4,939 individuals ages 12-18. LCA yielded a four-class solution: a) "Nonvictims" (N=4,274), b) "Traditional victims" (N=486), c) "Cyber-victims" (N=107), and d) "Traditional victims and cyber-victims" (N=72). These findings inform practitioners of the most prevalent types of victimization in the population of adolescents and facilitate the identification of individuals who are at risk of being victimized.