One of the challenges in Latent Semantic Analysis (LSA) is to decide which corpus is best for a specific application. Furthermore, there are many parameters in LSA, such as the size of the corpus, the weight (local or global) functions, number of dimensions to keep, etc. that are important in generating high quality LSA spaces. In this talk, I will provide a general method to measure similarity between semantic spaces. Using this method, one can evaluate semantic spaces (such as LSA spaces) that are generated from different set of parameters. The method we have developed are generic enough that it can also be used evaluate to other semantic spaces.