When the length of the test speech is sufficient, the information and discrimination of single feature is sufficient to complete the speaker recognition task. However, when the length of the test speech was very short, the performance of speaker recognition is decreased significantly due to the small data size and insufficient discrimination. Aiming at the problem of insufficient speaker information under the short speech condition, a short utterance speaker recognition algorithm based on multi-featured i-vector was proposed. Firstly, different acoustic feature vectors were extracted and combined into a high-dimensional feature vector. Then Principal Component Analysis (PCA) was used to remove the correlation of the feature vectors, so that the features were orthogonalized. Finally, the most discriminating features were picked out by Linear Discriminant Analysis (LDA), which led to reduce the spatial dimension. Therefore, this multi-featured system can achieve a better speaker recognition performance. With the TIMIT corpus under the same short speech (2 s) condition, the experimental results showed that the Equal Error Rate (EER) of the multi-featured system decreased respectively by 72.16%, 69.47% and 73.62% compared with the single-featured systems including Mel-Frequency Cepstrum Coefficient (MFCC), Linear Prediction Cepstrum Coefficient (LPCC) and Perceptual Log Area Ratio (PLAR) based on i-vector. For the different lengths of the short speech, the proposed algorithm provided rough 50% improvement on EER and Detection Cost Function (DCF) compared with the single-featured system based on i-vector. Experimental results fully indicate that the multi-featured system can make full use of the speaker's characteristic information in the short utterance speaker recognition, and improves the speaker recognition performance.