Response to intervention (RTI) models for identifying learning disabilities rely on the accurate identification of children who, without Tier 2 tutoring, would develop reading disability (RD). This study examined 2 questions concerning the use of 1st-grade data to predict future RD: (1) Does adding initial word identification fluency (WIF) and 5 weeks of WIF progress-monitoring data (WIF-Level and WIF-Slope) to a typical 1st-grade prediction battery improve RD prediction? and (2) Can classification tree analysis improve the prediction accuracy compared to logistic regression? Four classification models based on 206 1st-grade children followed through the end of 2nd grade were evaluated. A combination of initial WIF, WIF-Level, and WIF-Slope and classification tree analysis improved prediction sufficiently to recommend their use with RTI.