In today’s highly competitive business environment, the product varieties of firms tend to increase and the demand patterns of commodities change rapidly. Especially for high tech industries, the product life cycles become very short and the customer demand can change drastically due to the introduction of new technologies in the market (i.e., introduction by the competitors). These factors increase the need for more efficient scheduling strategies. In this thesis, a learning-based scheduling system for a classical job shop problem with the average tardiness objective is developed. The system learns on the manufacturing environment by constructing a learning tree and selects a dispatching rule from the tree for each scheduling period to schedule the operations. The system also utilizes the process control charts to monitor the performance of the learning tree and the tree as well as the control charts is updated when necessary. Therefore, the system adapts itself for the changes in the manufacturing environment and survives in time. Also, extensive simulation experiments are performed for the system parameters such as monitoring (MPL) and scheduling period lengths (SPL). Our results indicate that the system performance is significantly affected by the parameters (i.e., MPL and SPL). Moreover, simulation results show that the performance of the proposed system is considerably better than the simulation-based single-pass and multi-pass scheduling algorithms available in the literature