Analogue arithmetic operations are the most fundamental mathematical operations used in image and signal processing as well as artificial intelligence (AI). In-memory computing offers high performance and energy-efficient computing paradigm. To date, in-memory analogue arithmetic operation with emerging nonvolatile devices were usually implemented using discrete components, which limits the scalability and blocks large scale integration. Here, we experimentally demonstrate a prototypical implementation of in-memory analogue arithmetic operations (summation, subtraction and multiplication), based on in-memory electrical current sensing units using spin-orbit torque (SOT) devices. The proposed analogue arithmetic operation structures are smaller than the state-of-the-art CMOS counterparts by several orders of magnitude. Moreover, data to be processed and computing results can be locally stored, or the analogue computing can be done in the nonvolatile SOT devices, which were exploited to experimentally implement image edge detection and signal amplitude modulation with simple structure. Furthermore, we constructed an artificial neural network (ANN) with SOT devices based synapses to realize pattern recognition with high accuracy of ~95%.