An activity of daily living, ADL, monitoring system for monitoring ADLs of a person within an environment, wherein the ADL monitoring system comprises: a set of sensors each adapted to respond to an activity and to generate a sensor output signal representative of the activity; a data processing unit adapted to receive the sensor output signals and to process the sensor output signals, to: generate an activity density map which identifies the level or type of a particular activity within particular timeslots; generate a reference map which indicates a reference value or range of values of activity levels or types within the particular timeslots; compare the level or type of a particular activity in the individual timeslots of the activity density map with the reference spread of activity levels or types in the corresponding timeslots of the reference map; determine a size of correspondence of the level or type of activity arising in each timeslot of the activity density map with the reference spread of activity levels or types in the corresponding timeslots of the reference map to identify initial anomaly points; characterised in that the data processing unit is further configured to: for the initial anomaly points, perform a test of activity permutations to find timeslots of the activity density map which may be reordered to remove as many of the initial anomaly points as possible; and identify the remaining anomaly points as a first anomaly indication. a set of sensors each adapted to respond to an activity and to generate a sensor output signal representative of the activity; a data processing unit adapted to receive the sensor output signals and to process the sensor output signals, to: generate an activity density map which identifies the level or type of a particular activity within particular timeslots; generate a reference map which indicates a reference value or range of values of activity levels or types within the particular timeslots; compare the level or type of a particular activity in the individual timeslots of the activity density map with the reference spread of activity levels or types in the corresponding timeslots of the reference map; determine a size of correspondence of the level or type of activity arising in each timeslot of the activity density map with the reference spread of activity levels or types in the corresponding timeslots of the reference map to identify initial anomaly points; characterised in that the data processing unit is further configured to: for the initial anomaly points, perform a test of activity permutations to find timeslots of the activity density map which may be reordered to remove as many of the initial anomaly points as possible; and identify the remaining anomaly points as a first anomaly indication. generate an activity density map which identifies the level or type of a particular activity within particular timeslots; generate a reference map which indicates a reference value or range of values of activity levels or types within the particular timeslots; compare the level or type of a particular activity in the individual timeslots of the activity density map with the reference spread of activity levels or types in the corresponding timeslots of the reference map; determine a size of correspondence of the level or type of activity arising in each timeslot of the activity density map with the reference spread of activity levels or types in the corresponding timeslots of the reference map to identify initial anomaly points; characterised in that the data processing unit is further configured to: for the initial anomaly points, perform a test of activity permutations to find timeslots of the activity density map which may be reordered to remove as many of the initial anomaly points as possible; and identify the remaining anomaly points as a first anomaly indication. for the initial anomaly points, perform a test of activity permutations to find timeslots of the activity density map which may be reordered to remove as many of the initial anomaly points as possible; and identify the remaining anomaly points as a first anomaly indication.