Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modeling approximations from many sources, such as boundary conditions, geometrical simplifications, and numerical assumptions, result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed – justifiably during the design stage, prior to construction – are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly 20 years of research results from several doctoral thesis and 14 full-scale case studies in 4 countries are summarized. Originally inspired from research into model-based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-to-understand tool that is compatible with the context of the built environment.