This paper explores the integration of sensing and communication (ISAC) using Stacked Intelligent Metasurfaces (SIM), focusing on a scenario with multiple communication users and an extended target. The authors propose an optimization algorithm to jointly optimize the beamforming at the base station (BS) and the end-to-end transmission matrix of the SIM, aiming to minimize the Cramér-Rao Bound (CRB) for target estimation while satisfying minimum Signal-to-Interference-plus-Noise Ratio (SINR) constraints for communication users and maximum transmit power. The proposed algorithm, Multi-Layer Alternating Optimization (MAO), leverages Singular Value Decomposition (SVD) to relax the non-convex optimization problem. Experimental results using a hardware platform demonstrate the effectiveness of the proposed algorithm in both communication and sensing tasks, showing that increasing the number of layers in the SIM improves performance.This paper explores the integration of sensing and communication (ISAC) using Stacked Intelligent Metasurfaces (SIM), focusing on a scenario with multiple communication users and an extended target. The authors propose an optimization algorithm to jointly optimize the beamforming at the base station (BS) and the end-to-end transmission matrix of the SIM, aiming to minimize the Cramér-Rao Bound (CRB) for target estimation while satisfying minimum Signal-to-Interference-plus-Noise Ratio (SINR) constraints for communication users and maximum transmit power. The proposed algorithm, Multi-Layer Alternating Optimization (MAO), leverages Singular Value Decomposition (SVD) to relax the non-convex optimization problem. Experimental results using a hardware platform demonstrate the effectiveness of the proposed algorithm in both communication and sensing tasks, showing that increasing the number of layers in the SIM improves performance.