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The Clinch Token Transfer Test (C3t) and Step & Stroop Test (SST) are newly developed dual task assessments for Huntington’s Disease (HD). As these are formed of a number of items they produce numerous variables and it is unknown which of these have greatest discriminative ability for each assessment.To use machine learning classifiers to assess the discriminative ability of the tests and to determine which individual aspects are most important to retain.Controls (n=27) and manifest HD (n=36) participants performed the C3t and SST. The C3t records 30 variables including time to complete each item, number of errors made and task costs (the difference in performance between increasingly complex tasks). The SST records 20 variables including number of steps and Stroop accuracy. To determine the discriminatory power of the assessments two classifiers were constructed, one using variables from the C3t and another using variables from the SST. A feature selection algorithm was used to determine which assessment variables were most important to retain.The best performing SST classifier had a mean accuracy of 84% using the number of steps in the baseline Step task, the number of correct answers in the Stroop Congruent Baseline and the number of correct answers in the Stroop Incongruent Baseline. The best performing C3t classifier had a mean accuracy of 88% using the time taken for the C3t Dual Task, the time taken for the C3t Triple Task and the task cost (time) between Baseline and Dual Tasks.This study suggests that the C3t and SST are reasonably suitable for distinguishing between manifest HD and controls. Furthermore, the assessment complexity can be reduced as the optimal models required a fraction of the scores recorded. Future work will seek to a) reduce the complexity of the tests and b) explore potential test enhancements that may bolster classifier performance.