The work presented in this thesis focusses on new techniques for capture and analysis of hyperspectral data. Due to the three-dimensional nature of hyperspectral data, image acquisition often requires some form of movement of either the object or the detector. This thesis presents a novel technique which utilises a rotational line-scan rather than a linear line-scan. Furthermore, a method for automatically calibrating this system using a calibration object is described. Compared with traditional linear scanning systems, the performance is shown to be high enough that a rotational scanning system is a viable alternative. Classification is an important tool in hyperspectral image analysis. In this thesis, five different classification techniques are explained before they are tested on a classification problem; the classification of five different kinds of Chinese tea leaves. The process from capture to pre-processing to classification and post-processing is described. The effects of altering the parameters of the classifers and the pre and post-processing steps are also evaluated. This thesis documents the analysis of baked sponges using hyperspectral imaging. By comparing hyperspectral images of sponges of varying ages with the results of an expert tasting panel, a strong correlation is shown between the hyperspectral data and human determined taste, texture and appearance scores. This data is then used to show the distribution of moisture content throughout a sponge image. While hyperspectral imaging provides significantly more data than a conventional imaging system, the benefits offered by this extra data are not always clear. A quantitative analysis of hyperspectral imaging versus conventional imaging is performed using a rice grain classification problem where spatial, spectral and colour information is compared.