The process of the characteristic feature extraction numerical!} is one of the adopted techniques for the purpose of pattern recognition in the digital images, and the process of the characteristic feature extraction depending on the Co-occurrence matrices is one of the most important techniques for the purpose of pattern recognition in the textured images. This research aim to study the characteristic features for the textured images, eight characteristic features are selected to investigate the aim of this work. These selected features are: Maximum probability, entropy, homogeneity, cluster shade, cluster prominence, contrast, angular second moment, and the inverse difference moment. The characteristic features depending on the Co-occurrence matrix are extracted in two ways. In the first one, the characteristic features are extracted depending on average Co-occurrence matrices which be extracted for four angles (0°, 45°, 90°, and 135°). While in the second one, the characteristic features are extracted depending on the Co-occurrence matrix for each angle of the following angles (0°, 45°, 90°, and 135°). In this case, four values for each of the selected characteristic features are extracted. Then the average values for each of the characteristic features are extracted depending on the extracted four values. To study the effect of block size on the calculation of the statistical characteristic features, the statistical features are calculated for the whole image and for each block in the image after dividing the image into blocks with block size (32x32) and (64x64). In addition, to study the effect of quantization level on the calculation of the statistical characteristic features three values (8, 16 and 32) of quantization level are adopted in this research