The present investigation will evaluate the impact of Mel Frequency Cepstral Coefficients (MFCC) and the Perceptual Linear Predictors (PLP) coefficients, in the word error rate (WER) of systems dedicated to Automatic Speech Recognition (ASR) for Spanish in scenarios with unknown noise levels using the state-of-the-art tool-kit Kaldi. Conclusions will be given on how the selection of the acoustic feature MFCC improves the WER for identical acoustic models, providing evidence on the robustness of the MFCC in the task of ASR in noisy scenarios.