In this paper, a universal steganalysis method for spatial domain steganography technique in which features are extracted from prediction-error histogram is proposed. To find the relative features that alter due to embedding, the prediction-error histogram of images were used for analytic models. The current 12 spatial steganography methods with 100% embedding rate were used for testing. Through experimental analysis, it was discovered that the prediction-error histogram of cover images before and after embedding showed obvious differences. Therefore, the 8-D features extracted from prediction-error histogram made up the feature vector of the proposed steganalysis scheme. These features were further used for probabilistic neural network (PNN) classifier training and testing. The accuracy of the proposed steganalysis scheme was examined by using the NRCS image database. The experimental results showed the detection accuracy of the proposed universal steganalysis scheme for the 12 steganography methods reaches 98.2% at best. The comparison with prior technology shows that the proposed universal steganalysis scheme offers superior accuracy in detecting the 12 spatial domain steganography methods. Therefore, the proposed blind steganalysis scheme is very reliable for the detection of a spatial domain steganography method.