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 ; • determining , for each of the trained machine-learning models (11-13), 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 (11-13) whose figure of fitness (11a-13a) meets a predetermined criterion; and • mapping the record (3a-3c) of measurement data to the sought condition using the at least one selected trained machine-learning model (11-13), the method being characterized in that the selection of the at least one trained machine-learning model (11-13) is further based on a requirement for the accuracy and/or for the confidence of the assessment of the condition , and the different models (11-13) are cascaded in that: • for an initial screening of the plant , a fast cursory assessment is performed by a rather simple model (11-13); and • if this model (11-13) indicates that the condition may be abnormal, the condition is investigated further using a more computationally intensive model (11-13) 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 ; • determining , for each of the trained machine-learning models (11-13), 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 (11-13) whose figure of fitness (11a-13a) meets a predetermined criterion; and • mapping the record (3a-3c) of measurement data to the sought condition using the at least one selected trained machine-learning model (11-13), the method being characterized in that the selection of the at least one trained machine-learning model (11-13) is further based on a requirement for the accuracy and/or for the confidence of the assessment of the condition , and the different models (11-13) are cascaded in that: • for an initial screening of the plant , a fast cursory assessment is performed by a rather simple model (11-13); and • if this model (11-13) indicates that the condition may be abnormal, the condition is investigated further using a more computationally intensive model (11-13) 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 (11-13); and • if this model (11-13) indicates that the condition may be abnormal, the condition is investigated further using a more computationally intensive model (11-13) that works on more complex measurement data (3, 3a-3c). <EOS>