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In this paper, an artificial intelligence solution to diagnose faults before acquisition of submersible petroleum motor pump systems is presented. Proper fault identification is time consuming and demands highly trained human experts. The diagnosis system is intended to facilitate the work of the human component of this important process by replicating the decision of highly trained experts through a classifier. To perform the automatic diagnosis, firstly intermediate features are extracted as the vibration spectra. Subsequently, high level features are extracted and fed into a classifier that outputs the final diagnose. To validate our proposal and to select the best classifier (among K-Nearest-Neighbour, Random Forest, Support Vector Machine and Decision Tree) for this problem, we performed a comparative study using real data acquired in tests accomplished before acquisition of submersible motor pumps. Our dataset comprises thousands of entries of accelerometer sensors (vertically distributed along the particular system components) data labelled by an human expert to one of the considered scenarios (normal pump, faulty sensor, faulty pump with rubbing, misalignment or unbalance). Results have showed that the evaluated classifiers have equivalent performance for the given problem, and that the standardization procedure can improve the performance of some classifiers. The performance of the classifiers is sufficient to facilitate the work performed by humans and consequently reduce the time spent in the pump fault diagnosis process.