Accurate monitoring of tool wear during machining helps to avoid product quality problems caused by tool failure. To formulate tool wear monitoring models for different working conditions, it is necessary to adjust the parameters for each group of working conditions to ensure the accuracy. To reduce the number of parameter adjustment and ensure the prediction accuracy, the advantages of deep forest are combined, such as few hyperparameters, parameter insensitivity to the model and adaptive training process. A tool wear state prediction model with multi-sensor signals and autonomous feature selection for multi-conditions is established by using deep forest. The multi-sensor and wear data of TC18 milling process under three sets of different process parameters, and the open data in the predictive and health management (PHM) society 2010 high-speed CNC machine tool health prediction competition are utilized. For the three sets of working conditions, the prediction accuracy values of deep forest are 95. 35% , 96. 63% and 97. 06% , respectively, and 98. 95% on PHM data, which evaluate the high accuracy and applicability of deep forest for tool wear prediction under multiple working conditions. It provides a strong guidance for online monitoring technology in intelligent machining technology.