Symbolic model order reduction (SMOR) is a macromodeling technique that can be used to create reduced order models while retaining the parameters in the original models. Such symbolic reduced order models can be repeated evaluated (simulated) with greater efficiency for varying model parameters. Although the model order reduction concept has been extensively developed in the literature and widely applied in a variety of problems, model order reduction from a symbolic perspective has not been well studied. Several methods developed in this paper include symbol isolation, nominal projection, and first order approximation. These methods can be applied to models from having only a few parametric elements to many symbolic elements. Of special practical interest are models that have slightly varying parameters such as process related variations, for which efficient reduction procedure can be developed. Each technique proposed in this paper has been tested by circuit examples. Experiments show that the proposed methods are potentially effective for many circuit problems.