Hyperspectral data sets offer new possibilities for archaeological research as they record spectral signatures in many (often textgreater 100) contiguous and small spectral bands, whereas traditional multi-spectral sensors and cameras undersample the true signature. With this new data source, however, at least two broad problems need to be solved (assuming that the image geometry can be correctly handled). The first problem relates to the huge amount of available information that is not directly accessible to the human eye, making data mining approaches necessary. The second problem relates to data quality. Indeed, as the upwelling electromagnetic radiation is recorded in small bands that are only about ten nanometres wide, the signal received by the sensor is quite low compared to sensor noise and possible atmospheric perturbations. In the same way, the necessary high spatial resolution requires a small IFOV (instantaneous Field-of-View) further limiting the useful signal stemming from the ground. For these reasons, filter techniques are necessary. A user-friendly Matlab-based graphical user interface (GUI) was developed to help the image analyst (not necessarily a specialist in remote sensing nor in imaging spectroscopy) getting the most information out of the recorded 3D data cube. As the main focus for the GUI is archaeological prospection, the aim was to visualize the data highlighting possibly occurring crop or soil marks. Powerful filters based on the Whittaker smoother were implemented currently not available within commercial image processing software. The user can visualize the sequence of individual bands in an animated way, or look at the first few principle components. Shape information such as the red edge inflection point is derived from spectrally smoothed and oversampled signatures giving new insights into crop vigour/crop stress. Additionally, various standard and optimized hyperspectral vegetation indices were implemented. Areas can be highlighted having a similar spectral signature compared to a user-selected pixel or region of interest. The user can further test the usefulness of a large set of edge detection algorithms.