The organoleptic qualities of many herbs, spices, citrus fruits, conifers, and most flowers and fruits are attributed to monoterpenes, which are widely dispersed volatile chemicals found in the plant kingdom. Citronellal is a monoterpene with a variety of therapeutic uses in addition to its effectiveness as a mosquito repellent. In this article, the citronellal content of four citronella essential oils was determined by a polymer-modified quartz crystal microbalance (QCM) sensor and several chemometric techniques. Principal component analysis (PCA) demonstrated the clustering of these four essential oil samples. Using the sensor response data, three classifiers were developed: a k-nearest neighbors (KNN) model, an artificial neural network (ANN) with a back propagation multilayer perceptron (BPMLP) model, and a support vector machine (SVM). A better classification accuracy (79%) was obtained with SVM. Furthermore, the sensor response was correlated with the conventional gas chromatographic (GC) method using both principal component regression (PCR) analysis and partial least square regression (PLSR) analysis.