Abstract We present a methodology to predict the discrepancy in modeling dynamic system response for untested system inputs. In particular, the methodology focuses on multi-component systems, where discrepancy in the system response is caused by model form errors in some of the system components. We combine a deterministic time-series artificial neural network (ANN) with Bayesian state estimation to formulate a relationship between inputs to the system and the corresponding discrepancies in the model estimates of system responses. We also identify the system components affected by model form errors, and predict discrepancies even in unmeasured locations of the system. The proposed approach is illustrated on a linear multi-degree of freedom dynamic system, and on an air cycle machine – a multi-component dynamic system.