With the rise of deep learning, reflected by the creation of architectures such as Convolutional Neural Networks (CNNs), researchers are becoming increasingly interested in the utility and relevance of machines that can properly generate information about images in different languages. This thesis focuses on Microsoft Azure's Computer Vision API, specifically its functionality for image description in both English and Spanish applied to a corpus of flora pictures. To assess the accuracy of the API's captions, a combination of human and machine evaluation was used. Although the initial hypothesis was that the CNNs of the API were robust enough to generate pertinent captions in both languages, the evaluations seemed to indicate that the technology is not yet mature enough to accomplish this task. This exploratory study therefore serves as a reflection on the use of automated image captioning for multilingual purposes and of the potential and limits of this technology.