The ores, once extracted, undergo several stages of processing before they can be properly used. The fines of ores that, at the end of this stage, do not have the granulometry required to feed the reduction furnaces, pass through agglomeration processes to reach it, such as pelletizing and sintering. The material produced in one of the stages of the latter process is the focus of this work. These fines first go through a micro-agglomeration stage, which is fundamental to the process because many of the characteristics and properties of the sinter are function of the structure of the pre-heat treatment microcluster. It consists of a mixture of the sinter feed, water, fluxes and solid fuel (coke). There are 3 typical structures for a microcluster: quasiparticle, micropellet, and non-agglomerated particles. The present dissertation has developed an automatic routine in the FIJI image-processing program, based on optical microscope image processing and analysis, which is able to identify the particles of different granulometry that compose the sample, and classify them in the 3 classes mentioned above. After classification, the routine is able to extract attributes of the identified objects (percentage of each class, average circularity, average thickness), and to analyze the quasiparticle nuclei, classifying them as to the phase (hematite, magnetite, goethite and others). In addition, the routine presents all the data in the form of a pdf report, which also contains a listing of quasiparticles and micropellets in increasing order of size. This automatic classification eliminates the lack of reproducibility and subjectivity of the human operator, provides measures that would be untenable manually, allowing the forecast of the future characteristics of the sinter in a fully automatic fashion.