Estimating the current memory capacity of a neural network based recognition system is critical to maximally use the available memory capacity in memorizing new inputs without exceeding the limit of the capacity (catastrophic forgetting). In this paper, we propose a dynamic approach to monitoring a network's memory capacity. Prior works in this area have presented static expressions dependent on neuron count N, forcing to assume the worst-case input characteristics for bias and correlation when setting the capacity of the network. Instead, our technique operates simultaneously with the learning of a Hopfield network and concludes with a capacity estimate based on the patterns which were stored. By continuously updating the crosstalk associated with the stored patterns, our model guards the network against overwriting its memory traces and exceeding its capacity. We designed a fingerprint recognition system based on our dynamic estimation technique. With the experiment using NIST Special Database 10, the system achieves 2.7 to 8X larger memory-capacity as compared to the baseline systems using the static capacity estimates.