The use of ultrasonic arrays for imaging in NDT applications is now widespread. The last decade has seen significant research progress in parallel with industrial uptake. Arrays offer an intuitive view of the interior of a component from which geometric features and defects can be observed. When an ultrasonic array illuminates a defect the received signals depend on the defect reflectivity which is itself a function of both the incoming and scattered angles. In essence, the array illuminates the defects from a range of angles and thereby examines a small portion of the defect’s scattering matrix. These scattered signals are useful as they encode information about the characteristics of the defect. The question then is, given some array reflectivity measurements can the defect be characterised and sized uniquely? The full answer to this question is still unclear, but fortunately, in most NDT applications, something definite is known about the possible types of defect. This knowledge unlocks the problem and leads to the general approach described here in which array scattering data is compared to simulations of scattering from possible defects. The closest match is then the characterisation result. Here we show that using this approach, coupled with additional information about the range of possible defects, accurate characterisation is possible even for defects that are fractions of a wavelength in size. Recently it has been shown that the same ultrasonic array data that can be used for linear imaging and characterisation, also contains information about the nonlinearity of the defects. This future possibility represents a way to move beyond the fundamental limits imposed by linear scattering. The nonlinear information encodes new characterisation information such as crack tip closure which is crucial in structural integrity assessments. The exciting prospect is that this new information can be obtained from commercially available array equipment at little additional cost, a rare case of physics giving “something for nothing.