A method of predicting a plurality of parameter values distributed over at least a portion of a selected sample wafer, the method comprising: providing a plurality of known parameter values measured from a plurality of targets at specific measurement locations on a training sample wafer; training a neural network using the measured, known parameter values so that the trained neural network is configured to predict a plurality of predicted parameter values such that a subset of the predicted parameter values which correspond to the specific measurement locations are within a predefined error function of the corresponding measured, known parameter values; using the trained neural network to predict the predicted parameter values at the plurality of locations distributed across at least a portion of the selected sample wafer; and determining whether the selected sample wafer passes or fails based on the predicted parameter values predicted by the trained neural network; wherein the prediction operation is based on the known parameter values without depending on an optical property of a photolithography tool and without use of a model of the photolithography tool; and wherein training the neural network and using the trained neural network includes inputting an alignment metric (810, 908) from a photolithography tool into the neural network and the trained neural network, inputting at least one process metric (812, 910) that respectively characterizes a property of the training sample wafer and of the selected sample wafer into the neural network and the trained neural network, inputting target quality metrics (808, 906) of at least one of the targets of each of the training sample wafer and the selected sample wafer respectively into the neural network and the trained neural network, said target quality metrics including a noise metric that quantifies a background characteristic of the at least one target of the training sample wafer and of the selected sample wafer and a systematic metric of the at least one target of the training sample wafer and of the selected sample wafer, wherein the predicted parameter values comprise a plurality of overlay error values. providing a plurality of known parameter values measured from a plurality of targets at specific measurement locations on a training sample wafer; training a neural network using the measured, known parameter values so that the trained neural network is configured to predict a plurality of predicted parameter values such that a subset of the predicted parameter values which correspond to the specific measurement locations are within a predefined error function of the corresponding measured, known parameter values; using the trained neural network to predict the predicted parameter values at the plurality of locations distributed across at least a portion of the selected sample wafer; and determining whether the selected sample wafer passes or fails based on the predicted parameter values predicted by the trained neural network; wherein the prediction operation is based on the known parameter values without depending on an optical property of a photolithography tool and without use of a model of the photolithography tool; and wherein training the neural network and using the trained neural network includes inputting an alignment metric (810, 908) from a photolithography tool into the neural network and the trained neural network, inputting at least one process metric (812, 910) that respectively characterizes a property of the training sample wafer and of the selected sample wafer into the neural network and the trained neural network, inputting target quality metrics (808, 906) of at least one of the targets of each of the training sample wafer and the selected sample wafer respectively into the neural network and the trained neural network, said target quality metrics including a noise metric that quantifies a background characteristic of the at least one target of the training sample wafer and of the selected sample wafer and a systematic metric of the at least one target of the training sample wafer and of the selected sample wafer, wherein the predicted parameter values comprise a plurality of overlay error values.