Room segmentation in architectural floor plan documents is crucial for tasks such as property valuation, space optimization, and automated design analysis. Unlike natural images, floor plans consist of abstract and sparse visual elements that pose unique challenges for segmentation models. In this work, we investigate the applicability of the Segment Anything Model (SAM), a general-purpose foundation model, to room-level segmentation in floor plans. We evaluate SAM on two heterogeneous datasets: a private collection and the public R3D dataset. Our results demonstrate that SAM consistently outperforms other semantic segmentation state-of-the-art approaches, enabling accurate surface area computation with minimal user input. We further analyze the impact of prompt quantity and training source variability, confirming SAM’s robustness and practical utility for real-world applications in architectural analysis.