This paper presents an adaptive NN-RBF classifier developed for object recognition under continuously time-varying perceptual conditions. The classifier is a hybrid of a neural net and a control environment. Adaptability of the classifier involves processes of image analysis, reinforcement generation, and classifier modification. An NN-RBF classifier is applied to a single image of a sequence. A feedback reinforcement generation mechanism evaluates the classification results when compared to the previous images and activates classifier modification, if needed. Classifier modification selects a strategy and employs four behaviors in adapting the classifier's structure and parameters. The developed approach is tested on indoor and outdoor image sequences.