An effective way to improve the efficiency and extend the life of the machines is to determine the failures of the bearings during operation. Early detection of bearing failures also has critical importance in terms of production costs. Various maintenance methods are used to prevent the failures. Despite all the precautions, unexpected failures can occur and production operations can be failed. This situation, apart from conventional methods, requires a novel determination and diagnostic technique. In this study, artificial intelligence based methods are applied and models are developed in order to detect bearing failures early and to classify the type of failure. In the developed models, it is possible to detect the ring failures depending on different loads and bearing vibration information. In addition, a classification is carried out for the fault from the inner or outer race of the bearing. Determination of the fault, as well as the diagnosis of the class, will increase stability and productivity, especially in critical industrial applications.