A user-centered interface agent learns user preferences and typical behaviors and, based on what is learned, predicts the user's preferred user interface for different types of host computers. The interface agent consists of a learning program which operates on the user's primary computer, a shadow program which is installed on a Personal Digital Assistant (PDA), and a remote program which operates on host computers. The PDA transfers data between the primary and remote machines, and can also be used as the user's primary computer. On the primary computer, the agent learns a user's preferences automatically by observing the user's actions, requiring minimal initialization by the user. The learning algorithm may be statistical, rule-based, case-based, neural network, or employ any other technique for reasoning under uncertainty. The automated personalizing of a user interface configuration has a particular advantage for individuals with disabilities who require configuration before they can use a new computer system.