To maintain productivity and alertness, individuals must be thermally comfortable in the space they occupy (whether it is a cubicle, a room, a car, etc.). However, it is often difficult to non-intrusively assess an occupant’s “thermal comfort,” and hence most HVAC engineers adopt fixed temperature settings to “err on the safe side.” These set temperatures can be too hot or too cold for individuals wearing different clothing, and as a result lead to feelings of discomfort as well as wastage of energy. Since humans dress to target a comfortable thermal sensation, it is reasonable to assume that clothing is an important measure of current thermal sensation. To this end, we develop SiCILIA, a platform that extracts physical and personal variables of an occupant’s thermal environment to infer the amount of clothing insulation without human intervention. The proposed inference algorithm builds upon theories of body heat transfer and is corroborated by empirical data. SiCILIA was tested in a vehicle with a passenger-controlled HVAC system. Experimental results show that the algorithm is capable of accurately predicting an occupant’s thermal insulation with a mean prediction error of 0.07clo.