A common challenge in smart environments is tracking individuals throughout different environments. Reliable solutions to this problem often involve cameras, which pose significant privacy issues, or trackable tags such as RFID which require that individuals be ‘prepared’ for the environment. In this paper an exploratory study is presented that investigates the utility of portable visible range spectroscopy hardware for the purposes identifying individuals based on the spectral pattern of their clothing. This is done by assessing the accuracy of a data-driven machine learning models when differentiating clothing. These sensors have the potential to achieve similar success to using the colour histogram from camera tracking without the privacy issues, and without the need to pre-tag individuals as with RFID’.