Neurodegenerative and neurological diseases affect distinct, though overlapping neural systems. These patterns of pathology result in impairments in specific cognitive domains. The aim of this study was to evaluate the performance of an automated 20-minute touchscreen-based cognitive test battery assessing multiple cognitive domains in predicting clinical diagnosis. 249 (mean age =52.7) patients presenting to a tertiary referral centre were assessed using Cantab Insight: an automated computerised battery which measures episodic memory, working memory, attention, executive function and processing speed, alongside self-reported memory problems (20-point scale) and the Geriatric Depression Scale (GDS). Cognitive scores were adjusted for age, education and gender based on a large normative dataset. Patients were diagnosed according to consensus criteria following structured history, examination, and ancillary tests (MRI brain, HMPAO-SPECT and CSF examination). Diagnoses included a range of neurological and neurodegenerative diseases (e.g. Alzheimer's disease (AD), fronto-temporal dementia (FTD), Parkinson's disease(PD)). Performance on Cantab Insight was not used to inform diagnosis. We constructed a series of logistic classification models using performance on single or multiple cognitive domains, GDS and self-reported memory to predict diagnosis. Model performance was verified using k-fold cross-validation, and ROC curves and confusion matrices were used to assess the accuracy of the models. We focused on diagnosis of AD, PD, FTD as well as participants with subjective cognitive complaints, but no clinical diagnosis. As predicted, different diagnostic groups showed distinct patterns of impaired performance. Logistic regression and classification modelling showed that performance on these tests was able to predict diagnostic group membership with high accuracy (area under the curve > 0.8). For all diagnostic groups, the inclusion of multiple measures of cognition significantly and substantially increased classification accuracy. Cantab Insight is a brief, well-tolerated assessment of different cognitive domains. Logistic classifiers based on performance in these different domains were able to discriminate diagnostic groups with a high degree of accuracy, in an unselected, heterogeneous group patients presenting to a tertiary referral centre. These data support the use of multi-domain assessments of cognition in a clinical setting.