This seminar will provide an overview of structural health monitoring (SHM) research that is being undertaken at Los Alamos National Laboratory (LANL). The seminar will begin by stating that SHM should be viewed as an important component of the more comprehensive intelligent life-cycle engineering process. Then LANL's statistical pattern recognition paradigm for addressing SHM problems will be introduced and current research that is focused on each part of the paradigm will be discussed. In th is paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction, and (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from real world structures, it quickly becomes apparent that the ability to cleanse, compress, normalize and fuse data to account for operational and environmental variability is a key implementation issue when addressing Parts 2-4 of this paradigm. This discussion will be followed by the introduction a new project entitled 'Intelligent Wind Turbines' which is the focus of much of our current SHM research . This summary will be followed by a discussion of issues that must be addressed if this technology is to make the transition from research to practice and new research directions that are emerging for SHM.