Optical image analysis is a very convenient tool for obtaining comprehensive information about fine iron ore size fractions. Data can be obtained on mineral abundances, porosity, particle shape and ore textures with a high level of accuracy. A range of techniques has been used to characterise iron ore samples on a particle-by-particle basis. Automatic textural classification of iron ore particles was used to establish classes containing particles with very similar mineral composition and texture. Image analysis coupled with probe analysis and mineral density measurements provided information about the chemical composition and density of each particle class. The combination of these results enabled a "virtual feed" to be created, which can be a key input into a beneficiation unit model for predicting its performance. Identification and classification of the textural type of each particle was performed according to the CSIRO-Hamersley Iron Ore Group Classification Scheme. If more detailed classification is needed, further classification can be performed based on dimensional, chemical or mineral criteria, such as the presence of certain minerals in particles or total iron content. Some deficiencies of the current image analysis procedures and their further improvement and automation are also discussed. (C) 2007 Elsevier Ltd. All rights reserved.