Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. Reasoning techniques increase the accuracy of functionalfault diagnosis based on the history of successfully repaired boards. However, depending on the complexity of the product, it usually takes several months to accumulate an adequate database for training a reasoning-based diagnosis system. During the initial product ramp-up phase, reasoning-based diagnosis is not feasible for yield learning, since the required database is not available due to lack of volume. We propose a knowledge-discovery method and a knowledge-transfer method for facilitating board-level functional fault diagnosis. First, an analysis technique based on machine learning is used to discover knowledge from syndromes, which can be used for training a diagnosis engine. Second, knowledge from diagnosis engines used for earlier-generation products can be automatically transferred through root-cause mapping and syndrome mapping based on keywords and boardstructure similarities. Two complex boards in volume production and with a mature diagnosis system, and three new boards in the ramp-up phase, are used to validate the proposed knowledgediscovery and knowledge-transfer approach in terms of the diagnosis accuracy obtained using the new diagnosis systems.