This paper describes dynamic trade-off evaluation (DTE), a new technique that has been developed to improve the performance of real-time problem solving systems. The DTE technique is most suitable for automation environments in which the requirement for meeting time constraints is of equal importance to that of providing optimally intelligent solutions. In such environments, the demands of high input data volumes and short response times can rapidly overwhelm traditional AI systems. DTE is based on the recognition that in time-constrained environments, compromises to optimal problem solving (in favor of timeliness) must often be made in the form of trade-offs. Towards this end, DTE combines knowledge-based techniques with decision theory to 1) dynamically modify system behavior and 2) adapt the decision criteria that determine how such modifications are made. The performance of DTE has been evaluated in the context of several types of real-time trade-offs in spacecraft monitoring problems. One such application has demonstrated that DTE can be used to dynamically vary the data that is monitored, making it possible to detect and correctly analyze all anomalous data by examining only a subset of the total input data. In carefully structured experimental evaluations that use real spacecraft data and real decision making, DTE provides the ability to handle a three-fold increase in input data (in real-time) without loss of performance.