A system that could help a medical practitioner to diagnose a patient who is smoker or nonsmoker is needed. Smoker's melanosis could be used as one indicator to identify someone is a smoker or not. This study focuses on the development of a noninvasive system of smoker identification based on hyperspectral imaging. The developed system consists of a smoker's image acquisition instrument and image processing algorithm using spectral and spatial characteristics in the Visible and Near-Infrared (VNIR) range. The average pixel intensity at a spatial range is used as a feature that represents the relative reflectance at the wavelength of 400-1000 nm. The PCA method is used to reduce the dimensions (features) into five characteristic features. The SVM method is used to classify the feature into Smoker's Melanosis (SM) and normal pixel information. This experiment was using 45 samples consisting of 20 smokers and 25 nonsmokers. It was performed to test the performance of the developed system. The results show that the accuracy is 97.31%, misclassification rate (MR) is 2.69%, false-positive rate (FPR) is 0%, false-negative rate (FNR) is 5.83%, sensitivity is 94.17%, and specificity is 100%. In general, the system has worked to help diagnose a smoker.