Machine vision is increasingly important for many applications, such as object classification. However, relying on conventional RGB imaging is sometimes insufficient – the input images are just too similar, regardless of algorithmic sophistication. Hyperspectral imaging adds the extra dimension of wavelength to conventional images, providing a much richer data set. Rather than expressing an image using red, green, and blue (RGB) values at each pixel location, hyperspectral cameras instead record a complete spectrum at each point to create a 3D data set, sometimes referred to as a hyperspectral data cube. The additional spectral dimension facilitates supervised learning algorithms that can characterize visually indistinguishable objects – capabilities that are highly desirable across multiple application sectors.