Stereolithography (SLA), the first Rapid Prototyping (RP) technology developed, is an additive manufacturing process that enables the direct fabrication of a resin model from a three-dimensional Computer-Aided Design (CAD) model. In this process, each cross-sectional layer of the model is traced by a Ultraviolet (UV) laser beam on the surface of a photosensitive liquid polymer. During the fabrication of each layer, the contour is scanned first, followed by hatching of the enclosed area. The laser exposure cures the hatched region, thereby solidifying the material and forming the desired layer. The dimensional accuracy of parts manufactured by Stereolithography is influenced by several process parameters, among which the hatching strategy adopted for laser scanning of the resin cross-section is one of the most significant. Various hatching strategies have been proposed to optimize this process. In this paper, different hatching methods are introduced and discussed. Furthermore, based on the results of a previous study, the Stereolithography process is modeled using a Multilayer Perceptron (MLP) Artificial Neural Network (ANN). The modeling results indicate that, after layer thickness, the hatching strategy is the second most influential parameter affecting the dimensional accuracy of fabricated parts.