The increased availability of observation data from engineering systems in operation poses the question of how measurement data can be incorporated into finite element models. The statistical finite element construction (statFEM) provides a principled means to synthesise measurement data with finite element models [1]. The uncertainties present in the data, the mathematical model, and its finite element discretisation are accounted for using a Bayesian statistical framework. The posterior densities of the finite element solution, model misspecification error, and measurement noise are inferred from the data by updating their respective priors. The corresponding likelihood function depends on both the data and the finite element model. Among others, statFEM has been employed in the development of a digital twin for an operational self-sensing structure, demonstrating its practical applicability to real-world engineering systems [2]. Recent extensions of the statFEM framework include the efficient representation of random fields [3], sequential filtering in elastodynamic problems, and the treatment of uncertain geometries. We discuss statFEM as a foundational framework for the development of digital twins and outline its implementation for self-sensing engineering structures. References: 1.  Girolami M., Febrianto E., Yin G. and Cirak F., The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions, Computer Methods in Applied Mechanics and Engineering, 375, 113533, 2021. 2. Febrianto E., Butler L., Girolami M. and Cirak F., Digital twinning of self-sensing structures using the statistical finite element method, Data-Centric Engineering, 3, e31, 2022. 3. Koh KJ. and Cirak F., Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis, Computer Methods in Applied Mechanics and Engineering, 417, 116358, 2023.