Mine managers rely on their experience, section foremen's daily reports, and occasional time studies to make important production, maintenance, and forecasting decisions. This information is often subjective and imprecise, consumes expensive engineers' time, is often biased, and is restricted to short time periods. Managers need better information. Furthermore, if a recently purchased machine is being evaluated, a large increase (> 10 pct) in productivity may be obvious, but small increases are usually masked by other variables. In today's highly competitive markets, cumulative small changes are important, especially since managers have already exhausted most available, obvious avenues of improvement. A technique to separate these charges from effects of other variables and to quantify the target effect is necessary. Colorado School of Mines researchers are addressing this need by developing an intelligent decision support system. It provides key production and maintenance information to support management decisions by recognizing patterns in mining machine sensor data and representing knowledge about mine management, operations, layout, and equipment.