A fundamental goal of the analysis of fMRI data is to locate areas of brain activation that can differentiate various cognitive tasks. Traditionally, researchers have approached fMRI analysis through characterizing the relationship between cognitive variables and individual brain voxels. In recent years, multivariate approaches (analyze more than one voxel at once) to fMRI data analysis have gained importance. But in majority of the multivariate approaches, the voxels used for classification are selected based on prior biological knowledge or discriminating power of individual voxels. We used sequential floating forward search (SFFS) feature selection approach for selecting the voxels and applied it to distinguish the cognitive states of whether a subject is doing a reasoning or a counting task. We obtained superior classifier performance by using the sequential approach as compared to selecting the features with best individual classifier performance. We analyzed the problem of over-fitting in this extremely high dimensional feature space with limited training samples. For estimating the accuracy of the classifier, we employed various estimation methods and discussed their importance in this small sample scenario. Also we modified the feature selection algorithm by adding spatial information to incorporate the biological constraint that spatially nearby voxels tends to represent similar things.