A method for condition monitoring of a distributed drive-train comprising measurement of physical signals obtained from sensors attached to the components (1,2,3) being chosen from the drive-train, said signals are delivered to computer means for processing the measured data and performing data fusion processes, using a data from an information database containing at least one information system, characterized in that it comprises two stages of data fusion processes, the first stage consists of the determination of the local posterior probability of fault p c ( t i, s c ( t i)) of the individual components of the drive-train, using Bayesian Inference according to a formula: p c t i, s c t i = 1 A L c y c t i | s c t i p c − t i, s c t i where: t i is a time instant, s c ( t i) is a component condition s at a time instant t i, c:1,2,3,..., C, and C is the number of components, p c- ( t i, s c ( t i)) is the prior probability of fault for the individual component c, L c ( y c ( t i)| s c ( t i)) is a component likelihood function CLF for the individual component c, defined as L c y c t i | s c t i = ∏ k = 1 K L k c y k c t i | s c t i where: k:1,2,3,.., K, and K is the total number of indicators considered for each individual component, L k c y k c t i | s c t i is an Indicator Likelihood Function which give the probability that a fault is present, given calculated Indicator values, y k c t i represents the value of the indicator k of the component c at the time instant t i, y c (t i) is the vector of indicator values of the component c at the time instant t i, A is a normalizing constant, and the second stage which consists of the determination of global posterior probabilities of fault p ( t i, S ( t i)) of the drive-train, using Bayesian Inference according to a formula: p t i, S t i = 1 B L Y t i | S t i p − t i, S t i where: t i is a time instant, S ( t i) is a global condition S of the drive-train in the time instant t i, p - ( t i, S ( t i)) is the prior probability of fault for the drive-train, Y ( t i) is the vector of local posterior probabilities of fault for all the components in the drive-train, at the time instant t i, L ( Y ( t i)| S ( t i)) is the global likelihood function GLF for the drive-train, this GLF is obtained by multiplying a different set of overall likelihood functions OLF of the different components ∏ c = 1 C L c Y c t i | S t i, where Y c ( t i) is equal to the local posterior probability of fault for the component c defined as p c ( t i, s c ( t i)), B is a normalizing constant, and next on a base of determined values of global posterior probabilities of fault p ( t i, S ( t i)) assessing the condition of the drive-train by choosing the maximum value from the global posterior probabilities of fault p ( t i, S ( t i)) which maximum value serves as an indicator for the most likely fault present in the drive-train and triggers an alarm for the drive train. t i is a time instant, s c ( t i) is a component condition s at a time instant t i, c:1,2,3,..., C, and C is the number of components, p c- ( t i, s c ( t i)) is the prior probability of fault for the individual component c, L c ( y c ( t i)| s c ( t i)) is a component likelihood function CLF for the individual component c, defined as L c y c t i | s c t i = ∏ k = 1 K L k c y k c t i | s c t i where: k:1,2,3,.., K, and K is the total number of indicators considered for each individual component, L k c y k c t i | s c t i is an Indicator Likelihood Function which give the probability that a fault is present, given calculated Indicator values, y k c t i represents the value of the indicator k of the component c at the time instant t i, y c (t i) is the vector of indicator values of the component c at the time instant t i, A is a normalizing constant, and the second stage which consists of the determination of global posterior probabilities of fault p ( t i, S ( t i)) of the drive-train, using Bayesian Inference according to a formula: p t i, S t i = 1 B L Y t i | S t i p − t i, S t i t i is a time instant, S ( t i) is a global condition S of the drive-train in the time instant t i, p - ( t i, S ( t i)) is the prior probability of fault for the drive-train, Y ( t i) is the vector of local posterior probabilities of fault for all the components in the drive-train, at the time instant t i, L ( Y ( t i)| S ( t i)) is the global likelihood function GLF for the drive-train, this GLF is obtained by multiplying a different set of overall likelihood functions OLF of the different components ∏ c = 1 C L c Y c t i | S t i, where Y c ( t i) is equal to the local posterior probability of fault for the component c defined as p c ( t i, s c ( t i)), B is a normalizing constant, and next on a base of determined values of global posterior probabilities of fault p ( t i, S ( t i)) assessing the condition of the drive-train by choosing the maximum value from the global posterior probabilities of fault p ( t i, S ( t i)) which maximum value serves as an indicator for the most likely fault present in the drive-train and triggers an alarm for the drive train.