This paper discusses the performance of several sets of feature combinations in automatic speaker identification (ASI). A signification reduction in dimension, based on linear discriminant analysis (LDA), is obtained without loss of recognition performance. We show that pre-normalization further enhances performance. Four different combinations are investigated and the best combination is derived from MFCC, RASTA-PLP and their \Delta's. 1 INTRODUCTION A speaker recognition system typically consists of a feature extraction followed by a statistical pattern classifier. The feature extraction might generate LPC, mel or PLP [1] cepstral features from frames of input speech, each of length typically a few tens of milliseconds and each feature vector typically of size 10 to 14 coefficients. The ASI performance is mainly affected by the feature itself and its order together with the classifier, see for example [2] and [3]. It is common practice to utilise information from different feature...