Aiming at the problem of insufficient recognition accuracy of traditional tool wear monitoring methods under multi-physics coupling conditions and complex working conditions, this paper proposes a cross-working-condition tool wear identification method based on Mel-frequency cepstral coefficients (MFCC) and joint distribution alignment (JDA), designed to address the challenge of weak tool wear monitoring capability across varying operational conditions. First, spindle vibration signals are collected, and MFCC is employed to extract features from these vibration signals, compressing high-frequency information while preserving critical mid-to-low frequency characteristics. Second, a transfer learning model based on joint distribution alignment is constructed, which integrates source domain labeled data with target domain unlabeled data for joint training, thereby achieving knowledge transfer of tool wear patterns across different working conditions. This model aligns output distributions through cross-entropy loss, aligns feature distributions using maximum mean discrepancy (MMD) loss, and incorporates a self-training pseudo-label generation mechanism to optimize the utilization efficiency of target domain data. On the NASA milling dataset, the proposed method achieves a recognition accuracy of 93.33% in cross-working-condition scenarios involving variations in feed rate, cutting depth, and spindle speed, significantly outperforming traditional methods based on Fourier transform and wavelet transform. When applied to the CTB milling test, the model achieved a recognition accuracy of 93.25%. The proposed method effectively resolves the challenge of tool wear state recognition under cross-working-condition environments, providing reliable technical support for tool condition monitoring in industrial applications, and possesses significant engineering application value.