An increasingly common question arising in processing digital multi-channel data from spacecraft and aircraft scanner systems is “which channels contain the best information to separate the classes of interest to the user?”. This may be to identify the best single channel for separating classes in a black and white photographic or line printer output product, or the best three channels for a colour photographic presentation of the data, or the best ‘n’ channels to enter into a classification (being mindful of the ‘trade off’ between improved sub-class separability and increasing usage of computer space and time resources). The only valid way to approach separability using more than one channel is to consider it in multi-channel space utilizing the inter-channel relationship terms. Having defined the classes using some form of hierarchical ordering approach, such as that proposed by Andersonet al. (1972), the user may compile the statistical profiles of the classes of interest from the sensor multi-channel data. Based on these statistics a number of multi-channel separability indices may be derived. Each of these indices quantifies, on the basis of the user-defined multi-channel statistics, the degree of inter-class separability the user can expect as a function of subsets of channels drawn from the overall sensor channel set. This review considers some of the more common multi-channel indices of separability and presents the links between them. Their various properties, and some limitations, are also presented as is an operational approach to their use.