As the volume of electronically stored information continues to expand across computer networks, the need for intelligent access to on-line collections of multimedia documents becomes imperative. Examples of such collections are the World Wide Web, digital libraries and enterprise-wide information repositories. Machine learning offers an invaluable corpus of techniques, tools and systems that can help to solve effectively related problems, such as semantic indexing, contentbased search, semantic querying, integration of ontologies/knowledge bases into Internet search technologies, in order to develop a new generation of intelligent search engines. There has been a growing interest in augmenting or replacing traditional information filtering and retrieval approaches with machine learning techniques in order to build systems that can scale to the intrinsic complexity of the task. This issue was addressed in the workshop on “Machine Learning for Intelligent Information Access”, which was organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99).