This thesis presents a lightweight approach for isolated Italian Sign Language (LIS) recognition, leveraging Hand Energy Images (HEIs) and custom convolutional neural networks (CNNs) designed for efficiency and real-world adaptability. Several preprocessing strategies, including Gaussian blur, background lightening, and adaptive thresholding, were explored to enhance model robustness across controlled and unconstrained environments. Experiments conducted on a subset of the Italian Sign Language A3LIS-147 dataset and a real-world dataset recorded by the author revealed distinct trends: models trained without preprocessing achieved the highest performance on the controlled A3LIS test set, while models trained with adaptive thresholding and Gaussian blur achieved superior generalization to real-world data.