Two important components of a speaker identification system are the feature extraction and the classification tasks. First, features must be robust to noise and they must also be able to provide discriminating information that the classifier can use to determine the speaker's identity. Second, the classifier must take the features that have been extracted from a sentence and label them as corresponding to one of the enrolled speakers. However, sets of features may be even more beneficial than any single feature by itself. There may be information present in one feature that other features do not have. Therefore, we present analysis of features and fusion by employing probabilistic averaging and weighted majority voting. Weighted voting will require that the weights are determined in a non-heuristic methodology and are robust to data with a large amount of channel distortion. Results using the King database show that both fusion methods lead to enhanced performance.