The effectiveness of automated system management is dependent on the domain-specific information that is encoded within the management framework. Existing approaches for defining the domain knowledge are categorized into white-box and black-box approaches, each of which has limitations. White-box approaches define detailed formulas for system behavior, and are limited by excessive complexity and brittleness of the information. On the other hand, black-box techniques gather domain knowledge by monitoring the system; they are error-prone and require an infeasible number of iterations to converge in real-world systems. MONITORMlNlNG is a gray-box approach for creating domain knowledge in automated system management; it combines simple designer-defined specifications with the information gathered using machine learning. The designer specifications enumerate input parameters for the system behavior functions, while regression techniques (such as Neural Networks, Support Vector Machines) are used to derive the mathematical function that relates these parameters. These functions are constantly refined at run-time, by periodically invoking regression on the newly monitored data. MONITORMINING has the advantage of reduced complexity of the designer specifications, better accuracy of regression functions due to a reduced parameter set, and self-evolving with the changes in the system. Our initial experimental results of applying MONITORMINING are quite promising.