Computer-implemented method of Condition Monitoring, CM, for rotating machines, comprising the steps: - continuously receiving (S1) samples of input data based on at least one physical quantity over time of a rotating machine ; - updating (S2) in real-time M accumulator variables, where M >= 1, based on L samples including a current sample s n and at least one preceding sample S n-1 of the input data; - computing (S3) in real-time N spectral features, where N >= 1, based on the M accumulator variables and m supplemental variables, where m >= 1; and - determining (S4) a condition of the rotating machine based on the N spectral features, wherein the M accumulator variables are updated in real-time based on the L samples and Lenv samples including the current sample sn and senv,n, a first preceding sample sn-1 and Senv,n-1 and a second preceding sample sn-2 and senv,n-2 of the input data and of the envelope and the N spectral features are computed in real-time by means of the Goertzel Algorithm, GA, wherein the totality of the M accumulator variables is sufficient to determine the condition of the rotating machine, and wherein the m supplemental variables are temporarily needed for computing the N spectral features and the m supplemental variables are not based on the received samples of the input data. - continuously receiving (S1) samples of input data based on at least one physical quantity over time of a rotating machine ; - updating (S2) in real-time M accumulator variables, where M >= 1, based on L samples including a current sample s n and at least one preceding sample S n-1 of the input data; - computing (S3) in real-time N spectral features, where N >= 1, based on the M accumulator variables and m supplemental variables, where m >= 1; and - determining (S4) a condition of the rotating machine based on the N spectral features, wherein the M accumulator variables are updated in real-time based on the L samples and Lenv samples including the current sample sn and senv,n, a first preceding sample sn-1 and Senv,n-1 and a second preceding sample sn-2 and senv,n-2 of the input data and of the envelope and the N spectral features are computed in real-time by means of the Goertzel Algorithm, GA, wherein the totality of the M accumulator variables is sufficient to determine the condition of the rotating machine, and wherein the m supplemental variables are temporarily needed for computing the N spectral features and the m supplemental variables are not based on the received samples of the input data. wherein the M accumulator variables are updated in real-time based on the L samples and Lenv samples including the current sample sn and senv,n, a first preceding sample sn-1 and Senv,n-1 and a second preceding sample sn-2 and senv,n-2 of the input data and of the envelope and the N spectral features are computed in real-time by means of the Goertzel Algorithm, GA, wherein the totality of the M accumulator variables is sufficient to determine the condition of the rotating machine, and wherein the m supplemental variables are temporarily needed for computing the N spectral features and the m supplemental variables are not based on the received samples of the input data. <EOS>