Surface mining is the most common mining method worldwide, and open pit mining accounts for more than 60% of all surface output. Haulage costs account for as much as 60% of the total operating cost for these types of mines, so it is desirable to maintain an efficient haulage system. As the size of the haulage fleet being used increases, shovel productivity increases and truck productivity decreases, so an effective fleet size must be chosen that will effectively utilize all pieces of equipment. One method of fleet selection involves the application of queuing theory to the haul cycle. Queuing theory was developed to model systems that provide service for randomly arising demands and predict the behavior of such systems. A queuing system is one in which customers arrive for service, wait for service if it is not immediately available, and move on to the next server or exit the system once they have been serviced. Most mining haul routes consist of four main components: loading, loaded hauling, dumping, and unloaded hauling to return to the loader. These components can be modeled together as servers in one cyclic queuing network, or independently as individual service channels. Data from a large open pit gold mine are analyzed and applied to a multichannel queuing model representative of the loading process of the haul cycle.  The outputs of the model are compared against the actual truck data to evaluate the validity of the queuing model developed.