Computer-implemented method of indicating a failure of a manufacturing process, comprising the steps: a) receiving at least one input signal (I1..I5) based on at least one physical quantity monitored during the manufacturing process by means of at least one sensor (S1..S4); b) transforming the received at least one input signal (I1..I5) into at least one parameter (P1..P5), having a different domain than the received at least one input signal (I1..I5); c) deriving latent features (LF) based on the at least one parameter (P1..P5) by means of a machine learning system (MLS) which is trained on deriving latent features (LF) based on the at least one parameter (P1..P5) indicative of specific states of the manufacturing process; wherein states of the manufacturing process comprises idle phase, start-up phase, normal operation and at least one failure of the manufacturing process in a manufactured workpieces, wherein the MLS is a convolutional neural network, d) mapping , using a nonlinear dimensionality reduction technique, the derived latent features (LF), provided by one of the hidden layers of the convolutional neural network, into one of several distinct clusters (C1..C3) in a two dimensional cluster space, wherein the clusters (c1..C3) represent different states of the manufacturing process; and e) indicating a failure of the manufacturing process based on the different states of the manufacturing process, wherein the at least one monitored physical quantity is a pressing force (F) of a press and the at least one generated input signal (I1..I5) is a force-over-time signal and wherein the failure in the pressed workpiece is indicated as the failure of the manufacturing process. a) receiving at least one input signal (I1..I5) based on at least one physical quantity monitored during the manufacturing process by means of at least one sensor (S1..S4); b) transforming the received at least one input signal (I1..I5) into at least one parameter (P1..P5), having a different domain than the received at least one input signal (I1..I5); c) deriving latent features (LF) based on the at least one parameter (P1..P5) by means of a machine learning system (MLS) which is trained on deriving latent features (LF) based on the at least one parameter (P1..P5) indicative of specific states of the manufacturing process; wherein states of the manufacturing process comprises idle phase, start-up phase, normal operation and at least one failure of the manufacturing process in a manufactured workpieces, wherein the MLS is a convolutional neural network, d) mapping , using a nonlinear dimensionality reduction technique, the derived latent features (LF), provided by one of the hidden layers of the convolutional neural network, into one of several distinct clusters (C1..C3) in a two dimensional cluster space, wherein the clusters (c1..C3) represent different states of the manufacturing process; and e) indicating a failure of the manufacturing process based on the different states of the manufacturing process, wherein the at least one monitored physical quantity is a pressing force (F) of a press and the at least one generated input signal (I1..I5) is a force-over-time signal and wherein the failure in the pressed workpiece is indicated as the failure of the manufacturing process. <EOS>