Today, machine vision experiences large latency due to big data processing, which is a barrier to time-critical applications. To address this issue, in-sensor computing was presented in the past. Here, we present a scheme of computing in a magnetic tunneling junction (MTJ) sensor array for proof-of-principle. Using the MTJ sensor array, the functions of artificial neural network (ANN) classifiers and autoencoders were verified. The time for correct classification of one picture was less than . The power consumed in the sensor array can be decreased according to the square law without affecting the results. Our work shows universal circuits and algorithms to compute in resistance-style ANN image sensors with promising energy efficiency.