The origin of sampled tissue and characteristics of healthy organs is important for potential abnormality detection in veterinary medicine. Most often such information is given during the sampling process, but in some cases there is a possibility of mislabeling, especially in the education sector where some microscopic preparations might be made by students without proper knowledge or the inspected tissues are not of fully known origin. Occasionally, it is possible to determine the affiliation by searching characteristics of the organ in a sample; however, this is not always possible even for a skilled professional as some tissues vary too little between their counterparts in different species or even in different organs of the same species. Because of this, an automatic system able to perform such classification in a fraction of a second and with high accuracy can be helpful in such cases, especially considering the low cost of adding that solution to the current workflow. This paper presents a new dataset for healthy organ classification based on light microscope imagery containing 25 abstract classes of different organs of a few species. During the sampling process, 3680 images of healthy tissues were collected. Additionally, a custom deep learning architecture was created that is able to classify those samples between organs and species with a validation accuracy reaching 98.34%. Such performance is in some cases higher than that of a human specialist, especially when some examples have very small visual differences between one another or the classification is made on previously non-determining regions of the organ. Additionally, the collection of such a dataset provides a great opportunity for further work containing abnormality detection as it already provides information on the healthy organ description, which can be used for a deep learning model searching for illnesses or mutations. What is more, such a dataset and the corresponding artificial neural network constitute one of the first solutions of this kind in veterinary medicine, as most state-of-the-art papers focus on human medicine.