ALISA (Adaptive Learning Image and Signal Analysis) is an adaptive learning image and signal classification engine based on collective learning systems theory. Using supervised training, the ALISA engine builds a set of multi-dimensional feature histograms that estimate the joint PDF of the feature space for each trained class. Until now the histograms have been stored as multi-dimensional static arrays. To classify many different textures classes in images, however, the hist.ograms for only 3 or 4 features with limited precision could be stored in reasonable amount of RAM In the current research, 6 general-purpose features, one with a precision of 60 bins and the rest with 20 bins, were used to build a dynamically allocated sparse data structure instead of a complete static structure for each class. If complete histograms had been allocated as static structures, these 6 features would have required about 1152 million bins, which is not feasible. In contrast, during the training of the new dynamically allocated ALISA with 6 different classes (sky, water, skin, rose, evergreen, and grass), a total about 12,000,000 counts were accumulated during training, generating fewer than 150, 000 unique feature vectors. The results (which can also be viewed at http: / /www. seas.gwu. edul-pbocklindex. html) demonstrate the classification of several test images for each of the 6 trained classes. Much work remains to be done to optimize the new dynamically allocated ALISA classifier, but the initial results are encouraging.