A computer-implemented method for classifying one or more servers (112, 114) by server type in a networked computing system to institute server-type based monitoring and maintenance of the networked computing system, the method comprising: collecting server performance data for a first server over a time interval, by collecting data on a set of performance metrics of the first server, the set of performance metrics being related to consumption of resources including one or more of central processing unit, CPU, utilization, disk bytes reads, disk bytes writes, memory usage, net bytes reads, and net bytes writes, wherein the set of performance metrics is represented as a vector X = [X 1, X 2,..., X j..., X N], with j = 1 to N; determining (220, 230, 240) a covariance matrix signature of the first server, based on the collected server performance data, being a multivariate covariance matrix Σ of the set of N performance metrics for the first server, with the entries Σ (i, j) being the covariances cov (X i, X j) = E [(X i - μ i) (X j - μ j)] = E [X i X j] - μ i μ j, where the operator E denotes the expected mean value of its argument, and μ i = E (X i); comparing the covariance matrix signature of the first server to a covariance matrix signature of a second server of known server type, the covariance matrix signature of the second server being persisted in a data store and being retrieved from the data store; and determining a similarity of the covariance matrix signature of the first server to the covariance matrix signature of the second server, and based on the similarity, classifying the first server as being of the same server type as the second server; wherein determining a similarity of the covariance matrix signature of the first server to the covariance matrix signature of the second server includes determining absolute values of the differences between elements of the covariance matrix signature of the first server and corresponding elements of the covariance matrix signature of the second server, and when the absolute values of all the differences are less than a pre-defined threshold value, determining the covariance matrix signature of the first server and the covariance matrix signature of the second server to be similar. collecting server performance data for a first server over a time interval, by collecting data on a set of performance metrics of the first server, the set of performance metrics being related to consumption of resources including one or more of central processing unit, CPU, utilization, disk bytes reads, disk bytes writes, memory usage, net bytes reads, and net bytes writes, wherein the set of performance metrics is represented as a vector X = [X 1, X 2,..., X j..., X N], with j = 1 to N; determining (220, 230, 240) a covariance matrix signature of the first server, based on the collected server performance data, being a multivariate covariance matrix Σ of the set of N performance metrics for the first server, with the entries Σ (i, j) being the covariances cov (X i, X j) = E [(X i - μ i) (X j - μ j)] = E [X i X j] - μ i μ j, where the operator E denotes the expected mean value of its argument, and μ i = E (X i); comparing the covariance matrix signature of the first server to a covariance matrix signature of a second server of known server type, the covariance matrix signature of the second server being persisted in a data store and being retrieved from the data store; and determining a similarity of the covariance matrix signature of the first server to the covariance matrix signature of the second server, and based on the similarity, classifying the first server as being of the same server type as the second server; wherein determining a similarity of the covariance matrix signature of the first server to the covariance matrix signature of the second server includes determining absolute values of the differences between elements of the covariance matrix signature of the first server and corresponding elements of the covariance matrix signature of the second server, and when the absolute values of all the differences are less than a pre-defined threshold value, determining the covariance matrix signature of the first server and the covariance matrix signature of the second server to be similar.