This paper investigates an approach that combines ensemble methods with graphical models to analyse multiple sensor measurements in the context of mine automation. Drill sensor measurements used for drilling automation have the potential to provide an estimate of the subsurface geological properties of the rocks being drilled. A Boosting algorithm is used as a local classifier mapping drill measurements to corresponding geological categories. A Conditional Random Field then uses this local information in conjunction with neighbouring measurements to jointly reason about their categories. Model parameters are learned from training data by maximizing the pseudo-likelihood. The probability distribution of classified borehole sections is calculated using belief propagation. We present experimental results of applying the method to classify rock types from sensor data collected from a semi-autonomous drill rig at an iron ore mine in Australia.