A cash handling machine comprising: a cash handling mechanism for receiving physical articles including cash; sensing equipment comprising one or more sensors arranged to sense one or more properties of each of the articles received by the cash handling mechanism; processing equipment comprising one or more processors, the processing equipment being arranged to receive corresponding sensor data from the sensing equipment indicative of the sensed properties of each of the received articles; and memory comprising one or more memory devices, the memory storing code arranged to run on the processing equipment; wherein the code comprises a plurality of neural networks (201a, 201b, 201c) arranged in a series ordered from first to last, each trained to classify which of a respective set of classes the articles belong to based on the corresponding sensor data, the set of classes of each respective neural network (201a, 201b, 201c) comprising a different respective subset of one or more respective recognized classes classifying a respective one or more types of cash recognised by the respective neural network (201a, 201b, 201c), and further comprising an unrecognized class into which all other articles other than the respective recognized types are classified; wherein the one or more types of cash recognized by each of the respective neural networks (201a, 201b, 201c) consist of one or more denominations of a respective currency, the respective currencies of at least some of the neural networks (201a, 201b, 201c) being different than one another, and wherein the code is configured so as, when run on the processing equipment, then for each of the articles received by the cash handling mechanism to perform the following algorithm starting with the first neural network in the series: i) use the neural network (201a, 201b, 201c) to classify the article into one of the respective set of classes based on the corresponding sensor data; then ii) if the classification determined in i) is one of the respective recognized classes, control the cash handling mechanism to accept the article, or do so subject to one or more further tests; but iii) if the classification in i) is the unrecognized class, then if the neural network (201a, 201b, 201c) is not the last in the series repeat i)-iii) with the next neural network (201a, 201b, 201c) in the series, but if the neural network (201a, 201b, 201c) is the last in the series control the cash handling mechanism to reject the article. a cash handling mechanism for receiving physical articles including cash; sensing equipment comprising one or more sensors arranged to sense one or more properties of each of the articles received by the cash handling mechanism; processing equipment comprising one or more processors, the processing equipment being arranged to receive corresponding sensor data from the sensing equipment indicative of the sensed properties of each of the received articles; and memory comprising one or more memory devices, the memory storing code arranged to run on the processing equipment; wherein the code comprises a plurality of neural networks (201a, 201b, 201c) arranged in a series ordered from first to last, each trained to classify which of a respective set of classes the articles belong to based on the corresponding sensor data, the set of classes of each respective neural network (201a, 201b, 201c) comprising a different respective subset of one or more respective recognized classes classifying a respective one or more types of cash recognised by the respective neural network (201a, 201b, 201c), and further comprising an unrecognized class into which all other articles other than the respective recognized types are classified; wherein the one or more types of cash recognized by each of the respective neural networks (201a, 201b, 201c) consist of one or more denominations of a respective currency, the respective currencies of at least some of the neural networks (201a, 201b, 201c) being different than one another, and wherein the code is configured so as, when run on the processing equipment, then for each of the articles received by the cash handling mechanism to perform the following algorithm starting with the first neural network in the series: i) use the neural network (201a, 201b, 201c) to classify the article into one of the respective set of classes based on the corresponding sensor data; then ii) if the classification determined in i) is one of the respective recognized classes, control the cash handling mechanism to accept the article, or do so subject to one or more further tests; but iii) if the classification in i) is the unrecognized class, then if the neural network (201a, 201b, 201c) is not the last in the series repeat i)-iii) with the next neural network (201a, 201b, 201c) in the series, but if the neural network (201a, 201b, 201c) is the last in the series control the cash handling mechanism to reject the article. i) use the neural network (201a, 201b, 201c) to classify the article into one of the respective set of classes based on the corresponding sensor data; then ii) if the classification determined in i) is one of the respective recognized classes, control the cash handling mechanism to accept the article, or do so subject to one or more further tests; but iii) if the classification in i) is the unrecognized class, then if the neural network (201a, 201b, 201c) is not the last in the series repeat i)-iii) with the next neural network (201a, 201b, 201c) in the series, but if the neural network (201a, 201b, 201c) is the last in the series control the cash handling mechanism to reject the article.