The human thermal system maintains a core body temperature of 36.7°C. Any deviation from this causes considerable discomfort. The arteries and veins play a significant role in transferring the temperature to maintain the thermal comfort of the human system. Thus the aim of this work was to bring out the potential of thermal imaging in diagnosing the cardiovascular disease in comparison with the standard techniques. The average skin surface temperature (SST (°C)) was compared with the standard bio-signaling method electrocardiograph (ECG) and the ECG-derived heart rate variability (HRV) time domain parameters. Standard biochemical assay and one minute ECG signal were obtained from known CVD men (n=10) and age-matched men (n=10) In each subject, infra-red (IR) thermogram of selected skin area of the body was obtained. From the ECG signal, RR interval was computed using Pan-Tompkins algorithm. HRV time domain variables were also calculated. When SST(°C) variables using static SST (°C) alone were utilized in the CAD model for evaluation of CVD, it was found that, Naive-Bayes classifier with variables ranking and selection by Wilcoxon method gave an accuracy of 90%, and its sensitivity and specificity were found to be 80% and 90% respectively. It was comparable to the obtained CAD model using ECG variables as a standard, whose accuracy was same, and its sensitivity and specificity were found to be 90% and 80% respectively. Also, a better accuracy of 95% (sensitivity-90%, specificity-75%) could be achieved in classifying the normal and CVD subjects when the static average SST (°C) was used along with the HRV time domain variables. Thus the inclusion of IR thermogram in the CAD model along with standard variables improves the accuracy of cardiovascular disease evaluation.