We will look at a relatively uninvestigated approach for solving large-scale nonlinear constrained optimization problems referred to as Successive Linear Programming (SLP). It is well known that the classical Active-Set Sequential Quadratic Programing (SQP) approach, although good for small to medium-scale problems, becomes ineffective for large-scale problems because of the difficulty of determining the active-set. The SLP method uses the SQP framework but attempts to more quickly identify the correct active-set for large-scale problems by solving a linear program (LP) to generate a working set. We will give an overview of the SLP method and discuss some of the algorithmic details.