Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for applying cognition in wireless networks is presented. Also, two machine learning techniques are used to create a hybrid cognitive engine. Furthermore, the concept of cognitive radio resource management along with some of the network applications are discussed. To evaluate the proposed approach cognition is applied to three typical wireless network problems: improving coverage, handover management and determining recurring policy events. A cognitive engine, that uses case-based reasoning and a decision tree algorithm is developed. The engine learns the coverage of a cell solely from observations, predicts when a handover is necessary and determines policy patterns, solely from environment observations. / Ph. D.