Prediction of the downstream processing performance of iron ore needs detailed information on mineral composition, textural peculiarities, particle density, porosity, mineral liberation and association and mineral grain size._x000D_
Optical image analysis and automated iron ore texture classification systems are a way of obtaining such information. Furthermore, this information can be used for predicting the output of specific processes and their combinations._x000D_
Several examples of the application of such an approach to process performance studies for iron ore are discussed. These include the application of classification by ore texture to modelling and optimisation of hydrocyclone performance, the effect of including textural information in modelling of sinter properties, and combining textural information with an experimental study of stirring and ultrasonic treatment of haematitic-goethitic iron ore fines to understand the deportment of fines and the breakdown/deagglomeration of particles. In all cases, the availability of textural information is important and helps provide a better prediction of process performance and/or understanding of the unit process.