Linear classifiers are widely used in machine learning due to their simplicity and computational efficiency. However, existing methods face limitations including sensitivity to outliers, restrictive assumptions, and computational instability. To address these challenges, we propose the Elliptic Separator (ES), a novel linear classifier based on geometric principles. The method employs Principal Component Analysis, affine transformations, and ellipsoid fitting to transform the feature space, enabling the determination of a maximum-margin boundary between clusters. The classification task is reduced to minimizing the distance between the origin and an ellipse, which simplifies computation by solving polynomial equations. Numerical experiments on 2D binary datasets demonstrate that the Elliptic Separator outperforms traditional linear classifiers, offering a stable and efficient alternative. Future extensions aim to generalize the approach to higher dimensions and evaluate its performance on real-world data.