In this chapter we have explored the robustness issues that arise in the identification of continuous-time systems from sampled data. A key observation is that the fidelity of the models at high frequencies generally plays an important role in obtaining models suitable for continuous-time system identification. The problems discussed above have been illustrated for both, deterministic and stochastic systems. Special attention was given to the identification of continuous-time autoregressive stochastic models from sampled data. We have argued that traditional approaches to this problem are inherently sensitive to high-frequency modelling errors. We have also argued that these difficulties can be mitigated by using the proposed FDML with restricted bandwidth.