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The small hyperspectral imager Compact High-Resolution Imaging Spectrometer (CHRIS) is the most important instrument for Earth observation included in the payload of the European Space Agency Third-Part Mission Project for On-Board Autonomy (PROBA)-1 satellite. This instrument has provided dozens of images in several target areas in the world, and a good number of acquisitions are available for the test site of Frascati and Tor Vergata, Italy. This paper reports several results concerning the generation of thematic maps obtained from CHRIS mode-3 imagery. The potential of the use of different configurations for the input vector exploiting multispectral, multiangular, and multitemporal measurements has been investigated, and the results have been evaluated and compared in terms of accuracy in the classification. The core of the decision task has been developed using the neural network methodology. Indeed, this approach is characterized by a particular ease in performing nonlinear mapping of a multidimensional set of inputs into the output one.