Students with early indicators of behavior risk have predictable, negative outcomes, and those with co-existing academic problems have significantly more negative outcomes. Identifying academic subclasses of students with behavior risk can inform integrated interventions and school-based problem-solving teams. In addition, identifying academic strengths among a population of children typically only differentiated by severity of maladaptive behaviors may offer insight into academic resiliency. Using a sample of 676 elementary school students identified as behaviorally at risk, latent class analysis of reading and math indicators was conducted. Results indicated a three-class structure was the best fit for these data, with Class 1 (25%) having the least academic risk, Class 2 (37%) as below average reading and math, and Class 3 (38%) with significant academic deficits. Class membership was found to significantly predict end of year statewide assessment performance. While those behaviorally at-risk students with co-occurring academic deficits were very likely to fail the end of year assessments (Class 3; 88%-99% failure rates), those with stronger academic skills (Class 1) were increasingly more likely to pass (47%-56% pass rates). Practical implications, including intervention selection, and future directions are discussed.