Alternative splicing is a mechanism for generating different gene transcripts (called isoforms) from the same genomic sequence. Finding alternative splicing events experimentally is both expensive and time consuming. Computational methods, in general, and machine learning algorithms,in particular, can be used to complement experimental methods in the process of identifying alternative splicing events. In this paper, we explore the predictive power of a rich set of features that have been experimentally shown to affect alternative splicing. We use these features to build support vector machine (SVM) classifiers for distinguishing between alternatively spliced exons and constitutive exons.Our results show that simple linear SVM classifiers built from a rich set of features give results comparable to those of more sophisticated SVM classifiers that use more basic sequence features. Furthermore, we use feature selection methods to identify computationally the most informative features for the prediction problem considered.