Itch is the primary symptom of inflammatory skin diseases such as atopic eczema and psoriasis, chronic renal failure, and chronic hepatic failure. Itch, like pain, is a subjective symptom. Characterizing itchy skin and skin prone to itch will lead to better understanding of these symptoms and ultimately better diagnosis and treatment of the underlining disease. The goal of our study is to determine whether the itchy skin region can be detected by hyperspectral imaging. We used an imaging system equipped with liquid crystal tunable filter for collecting hyperspectral images. A halogen lamp was used to illuminate the region of interest. Images were taken from 650 nm to 1100 nm wavelength with 10 nm interval. The hyperspectral images were collected from the forearms of two male and two female subjects. An approximate 50 mm × 50 mm region of interest was marked on the forearms before imaging. The itch was mechanically induced. Imaging was performed for three conditions with a 99% Spectralon white diffuse reflectance target on the side: before inducing itch (normal region), after inducing itch (test region), and after removing itch (control region). Two methods were used to detect the itchy and nonitchy regions from the normalized hyperspectral data. The first method used a spectral distribution exploration method. The second method used a supervised classification method, more specifically, a support vector machine (SVM) algorithm. The spectral distribution exploration method did not detect any different spectral signature for itchy region. On the other hand, the SVM classifier detected the itchy region with the surrounding non-itchy region. These results demonstrated the feasibility of using hyperspectral imaging combined with classification algorithms for detecting itchy skin region.