Olfaction is an ancient sensory system that allows us to access sophisticated information about our environment. Drawing inspiration from biology, carbon nanomaterials-based gas sensors combined with machine learning algorithms aim at replicating this performance and digitizing the sense of smell. This lecture is about gas discrimination and identification performance of carbon nanomaterials-based nanosensors. Functionalized carbon nanomaterials-based nanosensors were fabricated on multiple-channel gas sensor devices, and the sensing signal was acquired when exposed to various gases. The transient features of the gases were then extracted from the sensing signal and fed to a machine-learning algorithm to discriminate and identify the gases. The developed carbon nanomaterials-based electronic olfaction system shows excellent gas identification performance for different gases. This platform can be used to miniaturize e-noses, digitize odors, and distinguish various gases and volatile organic compounds (VOCs) for applications such as pathogen detection, environmental monitoring, and disease diagnosis.