Problem gambling rates are relatively low (2%–4%), yet these gamblers experience multisystemic negative consequences, high comorbidity, and low treatment utilization. We aimed to characterize variations in gambling patterns to inform prevention and intervention efforts. Using community advertising, we recruited a diverse sample of lifetime gamblers (n = 312) for telephone interviews for a psychometric study of the newly developed Computerized-Gambling Assessment Module. Latent Class Analysis enumerated and classified gambling subgroups by distinctive gambling patterns, based on 8 composite scales functioning as validators of latent class membership (i.e., diagnostic gambling symptoms, reasons for gambling, gambling “withdrawal-like” symptoms, problem gambling perceptions, gambling venues, financial sources for gambling, gambling treatment/help-seeking, and religiosity/spirituality). Based on a distinguishing clustering pattern driven by 6 of 8 factors, we found a 6-class solution was the best-fitting solution. Gambling severity is most strongly characterized not only by symptomatology but also by the number of gambling treatment/help-seeking sources used.