Abstract: The method provides object recognition procedure and a neural network by using the discrete-cosine transform (DCT) (4) and histogram adaptive quantization (5). The method employs the DCT transform with the added advantage of having a computationally-efficient and data-independent matrix as an alternative to the Karhunen-Loeve transform or principal component analysis which requires data-independent eigenvectors as a priori information. Since the set of learning samples (1) may be small, we employ a mixture model of prior distributions for accurate estimation of local distribution of feature patterns obtained from several two dimensional images. The model selection method based on the mixture classes is presented to optimize the mixture number and local metric parameters. This method also provides image synthesis to generate a set of image databases to be used for training a neural network.