One dimensional Doppler Ultrasound (DUS) is a commonly applied technique for fetal heart rate monitoring, but it can also be used to identify the timings of fetal cardiac valve motion. These timings are required to estimate the fetal cardiac intervals, which are fundamental and clinically significant markers of fetal development and well-being. Several methods have been proposed in previous studies to automatically identify the valve movement timings using 1-D DUS and fetal Electrocardiography (fECG) as a reference. However DUS is highly susceptible to noise and variable on a beat-to-beat basis. Therefore it is crucial to assess the signal quality to ensure its validity for a reliable estimation of the valve movement timings. An automated quality assessment can provide the operator with an online feedback on the quality of DUS during data collection. This paper investigates automated classification of the DUS signal quality using Naive Bayes (NB) classifier. The quality of 345 beats of DUS signals collected from 57 fetuses was assessed by four independent annotators and used for training and validation of the classifier. Using Fleiss kappa test, a fair agreement was found between the raters with overall κ = 0.3. The performance of the classification was tested by 10-fold cross validation. Results showed an average classification accuracy of 86% on training and 84% on test data.