Stroke is mainly caused by a narrowing of the carotid artery from a build-up of plaque. The risk of plaque rupture and subsequent stroke is dependent on plaque composition. Advances in imaging modalities offer a non-invasive means to assess the health of blood vessels and detect damage. However, the current diagnosis fails to identify patients with soft lipid plaque that are more susceptible to fissure, resulting in stroke. The aim of this study was to use waveform analysis to identify plaque composition and the risk of rupture. We have investigated pressure and flow by combining an artificial blood flow circuit with tubing containing different materials, to simulate plaques in a blood vessel. We used fat and bone to model lipid and calcification respectively to determine if the composition of plaques can be identified by arterial waveforms. We demonstrate that the arterial plaque models with different percentages of calcification and fat, results in significantly different arterial waveforms. These findings imply that arterial waveform analysis has the potential for further development to identify the vulnerable plaques prone to rupture. These findings could have implications for improved patient prognosis by speed of detection and a more appropriate treatment strategy.