Image processing and recognition methods are useful in many fields. According to situation, different techniques are used. For many years, methods based on optical Fourier transformation were very popular. Image recognition was performed generally by using optical correlators. Correlation techniques were strongly developed especially for military applications, but in many cases (industrial, biological and biomedical applications) these techniques suffer from a number of limitations. For these reasons, methods based on extraction and statistical processing of image features are more useful. Set of features can be extracted directly from an image (features based on image morphology, image moments etc.) or from image transforms (Fourier, Radon, Hough, Sine, Cosine etc.). The Fourier transformation is one of the most important in image processing. It can be simply performed by using an optical diffractometer. It allows to build image descriptors independent on image translation and after processing independent on image rotation. Diffractometers are very convenient in industrial and medical applications. Digital image processing and recognition were strongly developed on powerful workstations, however these procedures can also be implemented in PCs with DSP microprocessor cards or in situations where digital transforms used for image processing can be simply implemented and do not consume a lot of time. The example of biomedical image recognition performed in an optical way, by using a diffractometer, and in a digital system with a CCD camera will be described here.