Traditional risk factors for coronary artery disease (CAD) fail to adequately distinguish patients who have atherosclerotic plaques susceptible to instability from those who have more benign forms. Using plasma lipid profiling, this study aimed to provide insight into disease pathogenesis and evaluate the potential of lipid profiles to assess risk of future plaque instability. Plasma lipid profiles containing 305 lipids were measured on 220 individuals (matched healthy controls, n=80; stable angina, n=60; unstable coronary syndrome, n=80) using electrospray-ionisation tandem mass spectrometry. ReliefF feature selection coupled with an L2-regularized logistic regression based classifier was used to create multivariate classification models which were verified via 3-fold cross-validation (1000 repeats). Models incorporating both lipids and traditional risk factors provided improved classification of unstable CAD from stable CAD (C-statistic=0.875, 95% CI 0.874-0.877) compared with models containing only traditional risk factors (C-statistic=0.796, 95% CI 0.795-0.798). Many of the lipids identified as discriminatory for unstable CAD displayed an association with disease acuity (severity), suggesting that they are antecedents to the onset of acute coronary syndrome. Plasma lipid profiling may contribute to a new approach to risk stratification for unstable CAD.