Material Flow Analysis (MFA) is a useful method for modeling, understanding and optimizing material flow systems. MFAs incorporate databases of increasing size and quality and reveal more and more details about material flows into, within and out of given systems. As a consequence, MFAs are of increasing size and system structures are of increasing complexity. Due to differences in data quality, it is not always clear how reliable MFA results are. In this thesis, uncertainty and complexity in MFA are approached from a system-theoretical perspective and formalized as measures for characterizing and distinguishing material flow systems by their information content and system structure. MFAs are, in a graph-theoretical sense, understood as networks. The information content and system structure of these networks are described by formally linked metrics derived from the field of theoretical ecology. The structure of a system is computed according to the configuration of each individual flow in relation to its neighboring flows. Integrating measures for data quality, the uncertainty of quantitative MFAs before and after balancing is determined and the information content of material flow systems is quantified. As the applicability of statistical measures for the evaluation of data quality is typically limited in MFA, it is proposed to approximate data quality by means of multi-dimensional functions of MFA data attributes. Data attributes are data-associated annotations concerning statistical properties, meaning, origination and application of the data. These data attributes are systematically documented and evaluated in a data characterization matrix, which forms the basis for automated estimation of data quality and subsequent quantification of information content. Exemplarily, four material flow systems (phosphorus, palladium, plastics and aluminum) are analyzed, compared and distinguished in terms of their information content and system structure. The proposed procedures are useful for gauging the information content of MFAs and for analyzing their system structure by means of quantitative measures. They contribute to a better understanding of the informational basis of material flow systems. They enable material flow systems to be compared to one another and changes in the information content of material flow systems over time to be tracked. The proposed measures support the design of MFA systems, optimized use of available information, communication of MFA results, and decision making in scientific and institutional contexts in light of limited information.