Owing to the importance of rolling element bearings in rotating machines, condition monitoring of rolling element bearings has been studied extensively over the past decades. However, most of the existing techniques require large storage and time for signal processing. This paper presents a new strategy based on compressive sensing for bearing faults classification that uses fewer measurements. Under this strategy, to match the compressed sensing mechanism, the compressed vibration signals are first obtained by resampling the acquired bearing vibration signals in the time domain with a random Gaussian matrix using different compressed sensing sampling rates. Then three approaches have been chosen to process these compressed data for the purpose of bearing fault classification these includes using the data directly as the input of classifier, and extract features from the data using linear feature extraction methods, namely, unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA). Classification performance using Logistic Regression Classifier (LRC) achieved high classification accuracy with significantly reduced bandwidth consumption compared with the existing techniques.