Performance assessment in holistic approaches such as integrated natural resource management has to deal with a complex set of interacting and self-organizing natural and human systems and agents, all pursuing their own “interests” while also contributing to the development of the total system. Performance indicators must, therefore, reflect the viability of essential component systems as well as their contributions to the viability and performance of other component systems and the total system under study. A systems-based derivation of a comprehensive set of performance indicators first requires the identification of essential component systems, their mutual (often hierarchical or reciprocal) relationships, and their contributions to the performance of other component systems and the total system. The second step consists of identifying the indicators that represent the viability states of the component systems and the contributions of these component systems to the performance of the total system. The search for performance indicators is guided by the realization that essential interests (orientations or orientors) of systems and actors are shaped by both their characteristic functions and the fundamental and general properties of their system environments (e.g., normal environmental state, scarcity of resources, variety, variability, change, other coexisting systems). To be viable, a system must devote an essential minimum amount of attention to satisfying the “basic orientors” that respond to the properties of its environment. This fact can be used to define comprehensive and system-specific sets of performance indicators that reflect all important concerns. Often, qualitative indicators and the study of qualitative systems are sufficient for reliable performance assessments. However, this approach can also be formalized for quantitative computer-assisted assessment. Examples are presented of indicator sets for the sustainable development of regions, including the computer-based, time-dependent assessment of system performance using time-series data. Because of its systems-theoretical foundation, this approach avoids the problems of incompleteness and double-counting common in ad hoc methods of indicator selection.