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Neurocognitive dysfunctions are frequently identified in the addictions. These dysfunctions may indicate either diffuse dysfunction or may represent separate facets that have differential importance to the addiction phenotype. In a sample (n = 260) of alcohol and/or stimulant users and controls we measured responses across 7 diverse neurocognitive measures. These measures were Continuous Performance, Delay Discounting, Iowa Gambling, Stroop, Tower, Wisconsin Card Sorting, and Letter Number Sequencing. Comparisons were then made between the drug-dependent groups and controls using analysis of variance and also using a machine learning approach to classify participants based on task performance as substance-dependent or controls in 1 tree and as alcohol and/or stimulant users or controls in a second tree. The analysis of variance showed significant differences between groups on the Delay Discounting (p < .001), Iowa Gambling (p < .001), Letter Number Sequencing (p < .001), and Wisconsin Card Sorting (p < .05) tasks. The first classification tree correctly classified between substance-dependent or controls for 88.3% of participants and classified between alcohol and/or stimulant users or controls for 63.9% of participants. Delay discounting was the first split in both trees and in the substance-dependent and control tree. The analysis of variance results largely replicate previous findings. The machine learning classification tree analysis provides evidence to support the hypothesis that different measures of neurocognitive dysfunction represent different processes. Among them, delay discounting was the most robust in categorizing drug dependence.