Since the release of the Canadian WAIS-III normative data in 2001 (Wechsler, 2001), the clinical application of these norms has been limited by the absence of a method to estimate premorbid functioning. However, Lange, Schoenberg, Woodward, and Brickell (2005) recently developed regression algorithms that estimate premorbid FSIQ, VIQ and PIQ scores for use with the Canadian WAIS-III norms. The purpose of this study was to expand work by Lange and colleagues by developing regression algorithms to estimate premorbid GAI (Saklofske et al., 2005), VCI, and POI scores. Participants were the Canadian WAIS-III standardization sample (n = 1,105). The sample was randomly divided into two groups (Development and Validation group). Using the Development group, a total of 14 regression algorithms were generated to estimate GAI, VCI, and POI scores by combining subtest performance (i.e., Vocabulary, Information, Matrix Reasoning, and Picture Completion) with demographic variables (i.e., age, education, ethnicity, region of the country, and gender). The algorithms accounted for a maximum of 77% of the variance in GAI, 78% of the variance in VCI, and 63% of the variance in POI. In the Validation Group, correlations between predicted and obtained scores were high (GAI = .70 to .88; VCI = .87 to .88; POI = .71 to .80). Evaluation of prediction errors revealed that the majority of estimated GAI, VCI, and POI scores fell within a 95% CI band (93.5% to 97.0%) and within 10 points of obtained index scores (72.3% to 85.6%) depending on the subtests used. These algorithms provide a promising means for estimating premorbid GAI, VCI, and POI scores using the Canadian WAIS-III norms.