The atrio-ventricular (AV) node is the primary regulator of ventricular rhythm during atrial fibrillation (AF). Hence, ECG based characterization of AV node properties can be an important tool for monitoring and predicting the effect of rate control drugs. In this work we present a network model of the AV node, and an associated workflow for robust estimation of the model parameters from ECG. The model consists of interacting nodes with refractory periods and conduction delays determined by the stimulation history of each node. The nodes are organized in one fast pathway (FP) and one slow pathway (SP), interconnected at their last nodes. Model parameters are estimated using a genetic algorithm with a fitness function based on the Poincare plot of the RR interval series. The robustness of the parameter estimates was evaluated using simulated data based on ECG measurements. Results from this show that refractory period parameters R{min}{SP} and Delta R{SP} can be estimated with an error (meanpm std) of 10pm 22 ms and-12.6pm 26 ms respectively, and conduction delay parameters D{min,tot}{SP} and Delta D{tot}{SP} with an error of 7pm 35 ms and 4pm 36 ms. Corresponding results for the fast pathway are 31.7pm 65 ms, -0.3pm 77 ms, and 1 7pm 29 ms,43pm 109 ms. This suggest that AV node properties can be assessed from ECG during AF with enough precision and robustness for monitoring the effect of rate control drugs.