We investigate the effect of speech coding on automatic speaker recognition when training and testing conditions are matched and mismatched. Experiments used standard speech coding algorithms (GSM, G.729, G.723, MELP) and a speaker recognition system based on Gaussian mixture models adapted from a universal background model. There is little loss in recognition performance for toll quality speech coders and slightly more loss when lower quality speech coders are used. Speaker recognition from coded speech using handset dependent score normalization and test score normalization are examined. Both types of score normalization significantly improve performance, and can eliminate the performance loss that occurs when there is a mismatch between training and testing conditions.