A method for assessing at least one condition of industrial equipment, and/or at least one condition of a process running in an industrial plant, based on measurement data (3, 3a-3c) gathered by a plurality of sensors (2a-2c), comprising: • obtaining at least one record (3a-3c) of measurement data that describes an operating situation of the equipment, and/or of the process; • providing a plurality of trained machine-learning models (11 - 13) that are each configured to map the record (3a-3c) of measurement data to an indication of the condition (50c, 51a); • determining, for each of the trained machine-learning models, a figure of fitness (11a-13a) to assess the situation described by the record (3a-3c) of measurement data; • selecting at least one trained machine-learning model whose figure of fitness (11a-13a) meets a predetermined criterion; and • mapping the record (3a-3c) of measurement data to the sought condition (50c, 51a) using the at least one selected trained machine-learning model, the method being characterized in that the selection of the at least one trained machine-learning model is further based on a requirement for the accuracy and/or for the confidence of the assessment of the condition (50c, 51a), and the different models are cascaded in that: • for an initial screening of the plant, a fast cursory assessment is performed by a rather simple model; and • if this model indicates that the condition may be abnormal, the condition is investigated further using a more computationally intensive model that works on more complex measurement data (3, 3a-3c). • obtaining at least one record (3a-3c) of measurement data that describes an operating situation of the equipment, and/or of the process; • providing a plurality of trained machine-learning models (11 - 13) that are each configured to map the record (3a-3c) of measurement data to an indication of the condition (50c, 51a); • determining, for each of the trained machine-learning models, a figure of fitness (11a-13a) to assess the situation described by the record (3a-3c) of measurement data; • selecting at least one trained machine-learning model whose figure of fitness (11a-13a) meets a predetermined criterion; and • mapping the record (3a-3c) of measurement data to the sought condition (50c, 51a) using the at least one selected trained machine-learning model, the method being characterized in that the selection of the at least one trained machine-learning model is further based on a requirement for the accuracy and/or for the confidence of the assessment of the condition (50c, 51a), and the different models are cascaded in that: • for an initial screening of the plant, a fast cursory assessment is performed by a rather simple model; and • if this model indicates that the condition may be abnormal, the condition is investigated further using a more computationally intensive model that works on more complex measurement data (3, 3a-3c). • for an initial screening of the plant, a fast cursory assessment is performed by a rather simple model; and • if this model indicates that the condition may be abnormal, the condition is investigated further using a more computationally intensive model that works on more complex measurement data (3, 3a-3c).