Driven by accelerated population growth and increasing globalization, the demand for food, particularly dairy and meat products, has risen to unsustainable levels, placing pressure on the livestock sector to adopt more efficient management strategies. Precision Livestock Farming (PLF) has emerged as a key response to this demand, developing tools based on individualized animal monitoring data. Among these, activity-monitoring collars have proven to be very promising, enabling a detailed tracking of each cow’s behavior in real time. This study explores the use of three dimensionality reduction techniques, t-SNE, UMAP and k-PCA, to characterize and classify the daily behavioral patterns of dairy cows that are part of an intensive farming system. Based on activity-monitoring collars data, it has been studied how each method captures the underlying structure of animal activity. Among the techniques assessed, UMAP proved to be particularly effective as a visual tool for identifying behavioral discrepancies between animals that are part of different farms. This characterization and classification capabilities are essential for the future development of predictive models improving herd management efficiency and supporting the sustainability of the livestock industry.