This paper focusses on the identification of the blockage fault in the inlet pipe of centrifugal pump over a range of speed using data obtained from different types of sensors. Acceleration, pressure and motor line current signatures taken using accelerometer, pressure transducer and current probe, respectively, and are used for identification of the pipe blockage level. Methodology is given for the blockage detection based on the multiclass classification using the deep learning algorithm at different blockage levels and speed of rotation of the pump. Importance of the multi-source data collection is emphasized based on the obtained results. Effect of the motor speed is also discussed when considered as an input feature to the classifier. It is observed that the use of combinations of different types of sensors help to identify the blockage level with better accuracy (close to 100% for many combinations). Blockage level prediction at each speed separately, is also given, which can be used for the fault diagnosis of single speed pumps. Finally, the performance of the classifier is tested using some unknown data (different from the data at training speed) to check the blockage prediction accuracy of the classifier.