The problem of object or scene recognition is often addressed by seeking geometric image properties that are invariant under changes in viewing conditions. An alternative, non-geometric, ratio method was described by Funt and Finlayson (IEEE Trans. Pattern Anal. Mach. Intell. 17,522, 1995) in which histograms of spatial ratios of colour RGB triplets from neighbouring image regions were used to recognise objects under changes in viewpoint and illumination. In this study, ratio indexing was extended from RGB images to hyperspectral images with a variable number of sensor channels distributed over 400-720 nm. Fifty natural scenes were used to generate test and reference images. For each number of sensors, independent random samples were drawn from each test image of a scene under either a daylight of correlated colour temperature of 25000 K or of 4000 K and matched against independent random samples drawn from each reference image of the scenes under a daylight of correlated colour temperature 6500 K. Matching was based on the intersection of multi-dimensional histograms of ratios of sensor signals in these samples Differences between match hit and false-alarm rates provided a measure of recognition performance. Results suggest that for small samples, indexing with five sensor channels has advantages over indexing with three sensor channels for the recognition of natural scenes.