Abstract: A method and system for detecting errors in a process such as laboratory analysis of patient specimens and generation of test results is described. The steps of the method include collecting data elements having a range of values from the process. The number of data elements having values within predetermined intervals of the range are then counted. The counts of the data elements are applied as inputs to nodes of a neural network, each count being applied to a node representing the predetermined interval corresponding to the count. Output is then generated from the neural network based on the inputs, the output indicative of whether an error in the process (such as bias error or a precision error) has occurred. If the technology is used with a laboratory instrument, the output is generated in real time and available immediately for automatic or manual correction of the instrument.