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A generic integrated sensory-intelligent system (ISIS) is developed for underwater acoustic signal-processing applications. ISIS constantly monitors the current acoustic channel conditions and smoothly integrates the outputs of the most appropriate signal-processing procedures or algorithms available to it for those conditions. The system is based on a generalization of a tuneable approximate piecewise linear (TAPL) model derived from the modified probabilistic neural network (MPNN). This model was designed to seamlessly integrate a set of local linear signal-processing algorithms within a given multidimensional data space. Depending on the input signal distortions, which are determined by environmental effects, ISIS automatically weighs and adds the outputs from a set of processing algorithms working in parallel. The weighting is related to the "closeness" of each algorithm to the sensed input signal characteristics or some other measured environmental state. A single tuning parameter is used to smoothly and seamlessly select appropriately among the parallel processing algorithm outputs. A very small tuning-parameter value selects the closest most appropriate algorithm output. At the other extreme, a fixed weighted average of all the algorithm outputs is produced with a very large value. Otherwise, a dynamic weighed average of all algorithm outputs is achieved with values in between. Some features and benefits of ISIS are demonstrated with an illustrative linear sweep chirp signal-detector estimation problem characterized by extremely variable Doppler conditions.