1. An automatic method of recognizing at a checkout system a produce item different from a number of different produce items to be sold, the method comprising: pre-training a multinomial regression based model with variations in sizes, shapes, colors, types, thermal data, and aroma data for a training set of produce items, and pre-training the multinomial regression based model with environmental factors present during the pre-training including with the environmental factors at least one factor for background lighting and at least another factor for humidity; providing a single classifier having a plurality of inputs, each input being adapted to receive produce data of a different modality, wherein at least one modality relevant to thermal information captured for the produce item, the thermal information providing data relevant to an internal structure and composition of the produce item, and wherein at least another modality relevant to aroma information captured as chemicals given off by the produce items and the aroma information provided by an olfactory sensor, the aroma information relevant to chemical signatures for the produce items; mapping the produce data to the respective input of the classifier by a computer executing produce recognition software; for each input, independently operating on the data relating to that input to create a feature set by the computer; comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set by the computer; combining all similarity description sets using a dedicated weighting function to produce a composite similarity description by the computer; and deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item by the computer by processing the multinomial regression based model and producing an identity for the produce item.