The compositional distribution of a population of particles is often referred to as the liberation spectrum. The challenge is to sensibly represent the vast amount of information contained in the liberation spectrum in a format that can be interpreted in a processing context. This paper describes one approach that can be used to represent the liberation spectrum, from compositional data obtained from automated SEM-based systems, and discusses how ore characteristics, such as mineral grade and grain size and distribution, can be interpreted. Ores from five different locations are compared using pyrite as the mineral of interest to demonstrate the approach. How this data might be used in a mineral processing context is also discussed, specifically with reference to how this approach may be used to determine grinding targets for separation processes. (C) 2014 Published by Elsevier Ltd.