Based on the objects’ sparse features, the compressive sensing imaging system has the unique advantage of breaking the Nyquist sampling theorem, and the target image can be reconstructed from very few random coded observations. The system is characterized by simple coding and complex decoding. It is difficult to meet the increasing real-time requirements in application because of the large time consumption by the iterative optimization algorithms. Therefore, it is a powerful way to improve the efficiency by bypassing the complex reconstruction process and extracting the target information directly from the random measurement data. In this paper, based on MNIST handwritten digital character database as an example, the object recognition method from random measurements of compressive sensing camera is explored. Firstly, the training samples in the MNIST database are coded with the observation of the random Bernoulli measurement matrix. And then the K-nearest neighbor classifier is constructed on the standardized samples, the measurements in the same measurement matrix of the target sample are put in the classifier, given the target recognition results. The experimental results show that the average recognition rate is 82.8% under the sampling rate of 0.1, and the total time to process 500 images is 0.063s. In contrast, the experiment of the traditional method by first reconstructing and then recognizing is conducted, the average recognition rate is 84.3%，and the total time to process 500 images is 48.2s. The proposed method is close to the traditional strategy in recognition accuracy, but the computational efficiency has been greatly improved (765 times), with great practical value.