Automated mineralogy methods and tools, such as the Mineral Liberation Analyser (MLA) and the QEMSCAN, are now widely used for ore characterization, process design and process optimization. Several case studies published recently demonstrate that large gains can be obtained through grinding and flotation optimization guided by automated mineralogy data. However, since automated mineralogy can only provide the information pointing to where the process gains can be made, it does not directly impact the production gain. Thus the question is often asked: how to value the contribution of automated mineralogy to process improvement at a particular plant. This appears to be a difficult question to answer. On close examination however, it is found that this is essentially a question of the value of information and this is reasonably well documented in various other industries. Hubbard, 2010, in chapter 7 "Measuring the Value of Information", dealt with exactly this type of problem. The value of information is the reduced risk of an investment and opportunity loss. The methods Hubbard developed can be applied to estimate the value of automated mineralogy, as well as metallurgical test work, both producing information that reduces the risk of investment.This paper first introduces Hubbard's theory on the value of information and how to measure it. It then applies his methods to estimate the value of automated mineralogy, using Anglo Platinum's fine grinding project as an example. In the end, a general model is developed to allow the simulation of the value of automated mineralogy in different mining operations constrained by different parameters. (C) 2014 Elsevier Ltd. All rights reserved.