In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line directly in industrial process situations. So a soft-sensor technology is needed for this purpose. The soft-sensor model is developed in the experiment by an adaptive neuro-fuzzy inference system (ANFIS) which has a remarkable ability of learning and generalization. Based on the analysis of the technologic mechanism of jigging bed, the structure of the ANFIS is established to build the soft-sensor model of loose status estimation. The ANFIS is trained by a hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, which indicate that the ANFIS has better abilities of learning and generalization than the RBF and the BP networks. Thus, it is possible that the loose status of the jigging bed can be estimated on-line by using ANFIS. In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line directly in industrial process situations. -sensor technology is needed for this purpose. The soft-sensor model is developed in the experiment by an adaptive neuro-fuzzy inference system (ANFIS) which has a remarkable ability of learning and generalization. Based on the analysis of the technologic mechanism of jigging The structure of the ANFIS is established to build the soft-sensor model of loose status estimation. The ANFIS is trained by a hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, which indicate that the ANFIS has better abilities of learning and generalization than the RBF and the BP networks. Thus, it is possible that the loose status of the jigging bed can be estimated on-line by using ANFIS.