Machine faults cause high financial losses for industrial companies. Machine faults stop the process flow, and are associated with idle time and additional expenses due to unexpected corrective maintenance. Depending on the fault, the idle time can span from short time actions to long inactivity periods for the machine. This idle time can risk a customer shipment, subsequently putting at risk the customer trust. A complex interaction between process variables cannot be easily traced using conventional condition monitoring approaches. There is an increasing trend to use machine learning methods to assess condition based maintenance. This paper proposes a mobile App for the fault detection assessment of industrial machinery. A proposal for the design and implementation of the mobile is presented, as well as the methodology followed for the fault detection assessment. The fault detection assessment is adressed using machine learning techniques, specifically a majority voting classifier. This majority voting classifier combines the predictions of three classification methods. The results show that the majority voting approach improves the performance of the fault detection assessment in comparison with the invidual classification methods.