A real-world intelligent system consists of three basic modules: environment recognition, prediction (or estimation), and behavior planning. To obtain high quality results in these modules, high speed processing and real time adaptability on a case by case basis are required. In each of the above mentioned modules, many different algorithms and algorithms networks exists and provide various performances on a case by case basis. Thus, a mechanism that for any of the three computational stages selects the best possible algorithm is required. We propose a platform based on the algorithm selection approach to the problem of natural image understanding. This selection mechanism is based on machine learning; a bottom-up algorithm selection from real-world image features and a top-down algorithm selection using information obtained from a high level symbolic world description and algorithm suitability. To accommodate the highspeed processing requirements, the high-frequency of real-time reconfiguration and a low-cost of implementation, we are using present a novel dynamic reconfigurable VLSI processor for real-time adaptation of the algorithm selection. The new architecture includes a fine-grain Digital Reconfigurable Processor, a distributed configuration memory to solve the data transfer bottleneck and an intra-chip packet routing scheme to reduce the size of the configuration memory.