Abstract: An intelligent system recommends a wireless product using a value-based framework. A product recommendation engine creates and delivers a survey requesting information from a user regarding wireless products needs and objectives. The user's response is captured and stored. The user's response is processed by an evaluation engine in conjunction with a logic engine for applying rules to reach a set of wireless products alternatives. The evaluation engine then enables the user to compare product attributes to narrow the list of alternatives. The product recommendation engine learns from itself, continually adding new inferences into its rule base. As new products are introduced, the product recommendation engine reviews previous recommendations to alert the user if the newly-introduced product better meets the user's needs. When a product is recommended to a user, an explanation engine explains the product recommendation based on the product's attributes and the user's objectives.