An error diagnosis method comprising: a parameter value obtaining step of obtaining parameter values of multiple parameters contained in at least one of an input signal (Si) to be input to a target machine targeted for error diagnosis or an output signal (So) output from the target machine; an error detection step of detecting, using a multidimensional statistical technique, whether or not error is caused at the target machine based on the parameter values obtained at the parameter value obtaining step; and an error portion estimation step of estimating an error portion of the target machine based on characteristic data obtained at the error detection step, wherein: the multidimensional statistical technique is a Mahalanobis Taguchi method, at the error detection step, a Mahalanobis distance from a preset unit space is calculated using the Mahalanobis Taguchi method, and it is detected whether or not the error is caused at the target machine based on the calculated Mahalanobis distance, and at the error portion estimation step, the error portion of the target machine is estimated based on the Mahalanobis distance as the characteristic data, the error portion estimation step includes: an item diagnosis step (S3a) of selecting, using the Mahalanobis Taguchi method, the parameters having influence on the Mahalanobis distance calculated at the error detection step, and a machine error estimation step (S3b) of estimating, using a Bayesian network, the error portion of the target machine based on the parameters selected at the item diagnosis step (S3a), wherein at the error portion estimation step, the item diagnosis step (S3a) is performed when it is determined that no error is caused at the target machine in the error detection step, and the error portion estimation step further includes a parameter storage step of storing the parameters selected at the item diagnosis step (S3a) when it is determined that no error is caused at the target machine in the error detection step, in the error portion estimation step, the machine error estimation step (S3b) is performed based on the parameters stored at the parameter storage step when it is determined that the error is caused at the target machine in the error detection step. a parameter value obtaining step of obtaining parameter values of multiple parameters contained in at least one of an input signal (Si) to be input to a target machine targeted for error diagnosis or an output signal (So) output from the target machine; an error detection step of detecting, using a multidimensional statistical technique, whether or not error is caused at the target machine based on the parameter values obtained at the parameter value obtaining step; and an error portion estimation step of estimating an error portion of the target machine based on characteristic data obtained at the error detection step, wherein: the multidimensional statistical technique is a Mahalanobis Taguchi method, at the error detection step, a Mahalanobis distance from a preset unit space is calculated using the Mahalanobis Taguchi method, and it is detected whether or not the error is caused at the target machine based on the calculated Mahalanobis distance, and at the error portion estimation step, the error portion of the target machine is estimated based on the Mahalanobis distance as the characteristic data, the multidimensional statistical technique is a Mahalanobis Taguchi method, at the error detection step, a Mahalanobis distance from a preset unit space is calculated using the Mahalanobis Taguchi method, and it is detected whether or not the error is caused at the target machine based on the calculated Mahalanobis distance, and at the error portion estimation step, the error portion of the target machine is estimated based on the Mahalanobis distance as the characteristic data, the error portion estimation step includes: an item diagnosis step (S3a) of selecting, using the Mahalanobis Taguchi method, the parameters having influence on the Mahalanobis distance calculated at the error detection step, and a machine error estimation step (S3b) of estimating, using a Bayesian network, the error portion of the target machine based on the parameters selected at the item diagnosis step (S3a), wherein an item diagnosis step (S3a) of selecting, using the Mahalanobis Taguchi method, the parameters having influence on the Mahalanobis distance calculated at the error detection step, and a machine error estimation step (S3b) of estimating, using a Bayesian network, the error portion of the target machine based on the parameters selected at the item diagnosis step (S3a), wherein at the error portion estimation step, the item diagnosis step (S3a) is performed when it is determined that no error is caused at the target machine in the error detection step, and the error portion estimation step further includes a parameter storage step of storing the parameters selected at the item diagnosis step (S3a) when it is determined that no error is caused at the target machine in the error detection step, in the error portion estimation step, the machine error estimation step (S3b) is performed based on the parameters stored at the parameter storage step when it is determined that the error is caused at the target machine in the error detection step.