Despite considerable advances over the last few decades in sensing instrumentation and information technology infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. One important reason for this being the mismatch between the growing diversity and complexity of machinery and equipment employed in industry and the historical reliance on “point-solution” diagnostic systems that necessitate extensive characterization of the failure modes and mechanisms (something very expensive and tedious). While these point solutions have a role to play, in particular for monitoring critical assets, generic yet adaptive solutions, meaning solutions that are flexible and able to learn on-line, could facilitate large-scale deployment of diagnostic and prognostic technology. The talk will start with an overview of requirements for autonomous diagnostics and prognostics and then present results from a variety of adaptive novelty-detection and model-based clustering methods.