The classification problem is the most applicable today; its implementations are face recognition, data grouping, and color classification. The primary objective of this study will be to address this last problem; that is to say: We will prove that it is possible to classify flowers using the well-known Iris data set made up of 150 samples with 4 attributes and 3 classes to identify. The novel idea in this study is the implementation of two different algorithms widely used for their high efficiency and simplicity: support vector machines and decision trees. This will lead us to observe how close both algorithms are from the viewpoint of the precision results that are high enough, in tune with the purpose of this study is high enough (very precise), and will be the problem to be solved, the one that finally determines the choice of the algorithm.