This paper proposed an approach of online content filtering system, which can filter unexpected content from Internet,
support searching, detecting, recognizing images, video and multimedia data. The approach consists of three parts: first is
texture feature extraction with quasi-Gabor filters. These filters are constructed in different directions and sizes in
frequency domain of images. This avoids convolution and multiplication with images spatially. Second, the extracted
features are sent to Kohonon neural networks to perform decreasing dimension. The outputs of Kohonon network are
then fed to a neural network classifier to get the final classification result. The proposed approach has been applied in our
content monitoring system, which can filter unexpected images and alarm by pre-defined requirement.