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In signal processing applications it is common to assume Gaussian statistics in the design of optimal signal processors. However, non-Gaussian processes do arise in many situations. For example, measurements reveal that radar clutter may be approximated by either Weibull, K-distributed, Lognormal, or Gaussian distributions depending upon the scenario. When the possibility of a non-Gaussian problem is encountered, the question as to which probability distributions should be utilized in a specific situation for modeling the data needs to be answered. In practice, the underlying probability distributions are not known a priori. Consequently, an assessment must be made by monitoring the environment. Another consideration is that radar detection problems can usually be divided into strong, intermediate, and weak signal cases. Hence, the system that monitors a radar environment must be able to subdivide the surveillance volume into weak background noise and clutter patches in addition to approximating the underlying probability distributions for each patch. This is in contrast to current practice where a single robust detector, usually based on the Gaussian assumption, is employed. The objective of this work is to develop techniques that monitor the environment and select the appropriate detector for processing the data. The main contributions are: (1) an image processing technique is devised which enables partitioning of the surveillance volume into background noise and clutter patches, (2) the Ozturk algorithm is used to identify suitable approximations to the probability density function for each clutter patch, and (3) rules to be used with an expert system shell under development at the University of Massachusetts and Boston University are formulated for monitoring the environment and selecting the appropriate detector for processing the data.