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Networked sensors are increasingly being used to perform tasks such as detection, source localization, and tracking. It is intuitive to expect a performance increase by completing these tasks with networked arrays. Quantifying the increase in performance requires a generalized analysis, which is typically done using the Cramer-Rao bounds (CRB). Previously, the CRB for multisource-single-array and multiarray-single-source models were derived, but not the general multiarray-multisource case. In this paper, the general case is modeled and is shown to reduce to previously published cases. The model and CRB derived in this paper serve as a benchmark to which current and future research in multiarray, multisource parameter estimation algorithms can be compared. It is proven that the CRB of a scalar parameter in a model containing K + 1 sources is always higher than the CRB of K sources. In addition, using numerical analysis, it is shown that adding sources to a multiarray model has unique effects that cannot be predicted from less general models. The number of sources and sensors, the geometry of the model (i.e., source and sensor locations), and the source and noise power levels all affect the CRB. The effect of changing model parameters is shown for several multiarray-multisource examples when the estimated parameter is source location.