We'll describe the design of Maintenance and Operations Recommender using Neural Networks. By integrating the information from Computerized Maintenance Management Systems (CMMS) and Energy Managementand Control System(EMCS), MORE can analyze and mine the data to make recommendations to the maintenance personnel and even automate the action if the precision of the prediction is good enough.Our previous research has shown that the relationship between the problem description and the actual action that's taken is non-linear. By introducing Neural Network to map this relationship, we improve the performance of the recommendation a lot. Also we experiment with methods to deal with the textual information in the system. One way is to use similarity index and the other is to use codified action.We demonstrate using codified action is a better choice to handle text.This work is part of the CITRIS Energy project, and the team is led by Dr. Clifford Federspiel.