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Traditionally, the analysis of Ballistic Missile Defense System (BMDS) effectiveness has been limited in fidelity due to the inherent complexity of the subject. Indeed, the BMDS battle management process involves monitoring and controlling the actions of many interacting participants (e.g. radar sensors, communications networks and interceptor missiles) in a process whereby a target moves from launch through sensor detection through intercept kill assessment. Because the actions of each participant may evolve independently, the battle management process functions as a true system-of-systems (SoS). Proper SoS analysis requires architecture level engineering, dealing with component functional allocation and inter-component interaction rather than the internal workings of individual participants. Although prior work has been identified that addresses BMD effectiveness at the SoS level, each method sacrifices analysis fidelity of both process elements and individual participants to enable timely decision making. This paper proposes a modeling and simulation (M&S) framework that supports architecture level analysis of the BMDS. The key innovation is the application of neural network surrogate models, which are representations of other high- or medium-fidelity M&S tools, and can be executed rapidly with negligible loss in fidelity. Surrogate models were created of a BMDS analysis tool that included multisensor target tracking and fusion codes. Results will show the benefit of integrating M&S to architecture level analysis. Specific examples include sensitivity of operational level metrics to formation of an integration tracking picture, and the enabling architecture level decision making.