Cutaneous ulcer caused by Leishmaniasis is a neglected disease which is more common in low-income areas. The main challenge in this disease is its diagnosis; the lack of specialized physicians makes its diagnosis difficult since quite often it can be miss-diagnosed with other type of skin ulcers such us venous, diabetes, and others. Given the previous mentioned facts, cutaneous ulcers caused by cutaneous Leishmaniasis require for the creation of novel tools that could assist its diagnosis. Hyperspectral and multispectral images measure the radiance reflected and emitted by a surface in hundreds or tens of spectral bands along the electromagnetic spectrum. This type of systems has been used for the analysis of cutaneous pathologies such us cancer, vitiligo, melasma, among others. With a set of classified hyperspectral images of cutaneous ulcers caused by different pathologies, it is possible to create an algorithm based on a multilayer neural network in order to achieve a classification of different types of ulcers. In this article we present the design of a feed-forward artificial neural network for the classification of cutaneous ulcers’ hyperspectral images in 4 kind of causes: occlusive vasculopathy, venous, Leishmaniasis, and diabetic. As result, a neural network structure is obtained that achieves a percentage of success higher than 72% in the classification of data.