Context-aware computing refers to a general class of mobile systems that can sense their physical environment, and adapt their behavior accordingly. Such systems are a component of a ubiquitous computing. Context aware computing makes systems aware of situations of interest, enhances services to users, automates systems and reduces obtrusiveness and customizes and personalizes applications. Mobile phones and PDAs are converging into mobile lifestyle devices that offer a wide range of applications to end users. Many of these applications will have the ability to adapt themselves to the user's situation, commonly referred to as context awareness. Context-aware services have been introduced into mobile devices, such as cellular phones. Context aware service recommendation engine for mobile is designed to automatically adopt its behavior to changing environment. To achieve this, an important issue to be addressed is how to effectively select services for adaptation according to the user's current context. In this paper, we propose an intelligent service recommendation model. We formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning .Bayesian Network to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this we have proposed maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed service recommendation engine and demonstrate its effectiveness

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