LA-UR-01-622: Submitted to ASME Journal of Dynamics Systems, Measurement and Control: Special Issue on Identification of Mechanical Systems 1 Structural Health Monitoring Using Statistical Pattern Recognition Techniques Hoon Sohn Engineering Sciences & Applications Division, Engineering Analysis Group, M/S C926 Los Alamos National Laboratory, Los Alamos, NM 87545 e-mail: sohn@lanl.gov Charles R. Farrar Engineering Sciences & Applications Division, Engineering Analysis Group, M/S C946 e-mail: farrar@lanl.gov Norman F. Hunter Engineering Sciences & Applications Division, Measurement Technology Group, M/S C931 e-mail: hunter@lanl.gov Keith Worden Department of Mechanical Engineering University of Sheffield Mappin St. Sheffield S1 3JD, United Kingdom e-mail: k.worden@sheffield.ac.uk This paper casts structural health monitoring in the context of a statistical pattern recognition paradigm. Two pattern recognition techniques based on time series analysis are applied to fiber optic strain gauge data obtained from two different structural conditions of a surface-effect fast patrol boat. The first technique is based on a two-stage time series analysis combining Auto-Regressive (AR) and Auto- Regressive with eXogenous inputs (ARX) prediction models. The second technique employs an outlier analysis with the Mahalanobis distance measure. The main objective is to extract features and construct a statistical model that distinguishes the signals recorded under the different structural conditions of the boat. These two techniques were successfully applied to the patrol boat data clearly distinguishing data sets obtained from different structural conditions.