The purpose of this study is to establish a model for the diagnosis of multiple micro-punch failures. The punch is assumed to a rigid body structure with a small change in the stiffness during the piercing process and its diameter is varied between Ø0.8–1.2 mm. Thus, the wearing trend of multiple punches in the piercing process and source of the interfered signals make it extremely difficult to analyze. The two major challenges that affect punch failure estimation are the poor signal-to-noise ratio within the factory environment and the rigid body mode disturbance in the signal. To acquire the vibratory signals of the piercing motion, uniaxial accelerometers were outfitted in the vertical direction on the progressive die. Since the piercing process is a series of highly nonlinear transient processes, the Ensemble Empirical Mode Decomposition (EEMD) is adopted as a decoupling operation tool for this kind of non-stationary signal. Furthermore, the dimension-reduced process can be manipulated by Intrinsic Mode Function (IMF) and a function representative of the feature is selected as the input for neural network model training. The training target is the most direct relationship with the product quality, the selected models are multi-layer perceptron and the back-propagation neural network (BPNN) of the error inversion algorithm. The artificial intelligence failure diagnosis of the piercing process is also realized.