Cardiovascular disease is one of the major problems in todays medicine and the number of patients increase worldwide. To treat these type of diseases, prior knowledge about function and dysfunction of the cardiovascular system is essential to identify the disease in an early stage. Mathematical modeling is a powerful tool for prediction and investigation of the cardiovascular system. It has been shown, that the Windkessel model, drawing an analogy between electrical circuits and fluid flow, is an eective method to model the human cardiovascular system. The aims of this work are the derivation of a computational cardiovascular model for the arm arteries, and to analyze the behavior of the vascular network structure by parameter sensitivity analysis. Sensitivity analysis is essential for parameter estimation and simplification of cardiovascular models. In optimal experiment design (OED) sensitivity analysis is used to construct experiments and corresponding models that allow the interpretation of cardiovascular measurements in an eective manner. In this paper we have applied sensitivity analysis to a linear elastic model of the arm arteries to find sensitive parameters and their confidence intervals that guide us to the estimation of cardiovascular network parameters. To calculate the percentage eect on the measurable state variables pressure and flow, with respect to percentage change in cardiovascular input parameters, we use norms. This method allows us to quantify and verify results obtained by sensitivity analysis. The sensitivities with respect to flow resistance, arterial compliance and flow inertia, reveal that the flow resistance and diameter of the vessels are most sensitive parameters. Those parameters play a key role in diagnoses of severe stenosis and aneurysms. In contrast, wall thickness and elastic modulus are found to be less sensitive.