Abstract Hundreds of materials, such as drugs, explosives, makeup, food additives, are in the form of powder. Recognizing such powders is important for security checks, criminal identifification, drug control, and quality assessment. However, powder recognition has drawn little attention in the computer vision community. Powders are hard to distinguish: they are amorphous, appear matte, have little color or texture variation and blend with surfaces they are deposited on in complex ways. To address these challenges, we present the fifirst comprehensive dataset and approach for powder recognition using multi-spectral imaging. By using Shortwave Infrared (SWIR) multi-spectral imaging together with visible light (RGB) and Near Infrared (NIR), powders can be discriminated with reasonable accuracy. We present a method to select discriminative spectral bands to signififi- cantly reduce acquisition time while improving recognition accuracy. We propose a blending model to synthesize images of powders of various thickness deposited on a wide range of surfaces. Incorporating band selection and image synthesis, we conduct fifine-grained recognition of 100 powders on complex backgrounds, and achieve 60%∼70% accuracy on recognition with known powder location, and over 40% mean IoU without known location.