Hyperspectral imaging is a technique which combines spectral and spatial imaging methods.The technology is used in remote sensing, medicine, agriculture and forensics just tomention a few. Non-remote systems are developed by using sensor designs different frompush-broom and whisk- broom methods, commonly found in remote sensing hyperspectralimaging systems. Images are commonly acquired by mounting various electronicallytunable filters in front of monochromatic cameras and capturing a range of wavelengthsto produce a spectral image cube. Illumination plays a major role during imaging, as boththe camera and electronically tunable filter may suffer low transmission at the ends of thevisible spectrum, resulting in a low signal to noise ratio.The work described in this thesis attempts to address two key objectives. The first wasto identify the main sources of errors in a common design of focal-plane hyperspectralimaging system and devise ways of compensating for these errors. Calibration and characterizationof a focal-plane hyperspectral imaging system included system noise characterization,stray-light compensation, flat field correction, image registration, input-outputfunction characterization and calibration verification.The other was to apply imaging techniques to hyperspectral images. This included scenerecognition using ratio indexing and spectral gradients. This comes from the underlyingidea that due to the large number of bands contained in hyperspectral images, more informationis available so better recognition results compared to RGB images. A novelapproach for obtaining ratios for ratio indexing is proposed in this thesis.The imaging of archived materials from University ofManchester’s John Rylands Librarywas also done. The aim was to produce high resolution hyperspectral images that will helpin identifying accurate matches for colours used in document restoration at the Library.