diff --git a/examples/009_histogram_fit/03_SplusBfit.py b/examples/009_histogram_fit/03_SplusBfit.py
new file mode 100644
index 00000000..1ed6e8a1
--- /dev/null
+++ b/examples/009_histogram_fit/03_SplusBfit.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+"""
+kafe2 binned Histogram Fit: signal on flat background
+=====================================================
+
+Fitting of models to histograms should generally be done using a "binned likelihood fit", 
+which takes into account the Poisson nature of the uncertainties on the bin entries.
+
+kafe2 comfortably provides such functionality via the classes HistContainer and
+HistFit. The "fit function" must be provided as a normalized probability density
+as the default. Normalizing to the number of entries in the histrogram is also
+possible by setting the option *density=False*; in such cases, an additinal
+parameter must be provided in the fit function to take care of the normalization.
+
+The example below is a minimalist one to determines the fraction of a gaussian-shaped
+signal on top of a flat background and may be used a the basis for any type of signal
+shape on a backgound distribution.
+
+Some notes:
+
+  - The *kafe2* class *HistContainer* provides various methods to create a histogram 
+    from input data. It accepts raw data, numpy historgrams or numpy arrays 
+    to directly set the bin contents
+  - The bin contents according to the model is determined by numerical integration
+    of the pdf from the left to the right bin edges. 
+  - The quality of fit, labeled as "-2ln L_R", is based on the ratio of the maximized 
+    likelihood of the data w.r.t the model function and the so-called "fully saturated 
+    model", which, in case of a histogram, represents a histogram exactly matching the 
+    bin entries observed in the data. This quanity becomes equal to the usual Chi2 value 
+    in case of sufficiently large numbers of entries per bin.     
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from kafe2 import HistContainer, Fit, Plot
+
+# parameters of data sample, signal and background parameters
+N = 200  # number of entries
+min = 0.0  # range of data, minimum
+max = 10.0  # maximum
+s = 0.8  # signal fraction
+pos = 6.66  # signal position
+width = 0.33  # signal width
+
+
+def generate_data(N=100, min=0, max=1.0, pos=0.0, width=0.25, signal_fraction=0.1):
+    """generate a random dataset:
+    Gaussian signal at position p with width w and signal fraction s
+    on top of a flat background between min and max
+    """
+    # signal sample
+    data_s = np.random.normal(loc=pos, scale=width, size=int(signal_fraction * N))
+    # background sample
+    data_b = np.random.uniform(low=min, high=max, size=int((1 - signal_fraction) * N))
+    return np.concatenate((data_s, data_b))
+
+def s_plus_bPDF(x, mu=3.0, sigma=2.0, s=0.5):
+    """(normalized) pdf of a Gaussian signal on top of flat background"""
+    normal = np.exp(-0.5 * ((x - mu) / sigma) ** 2) / np.sqrt(2.0 * np.pi * sigma**2)
+    flat = 1.0 / (max - min)
+    return s * normal + (1 - s) * flat
+
+def s_plus_b(x, Ns = 200, mu=3.0, sigma=2.0, Nb = 50.):
+    """Gaussian signal on top of flat background"""
+    normal = np.exp(-0.5 * ((x - mu) / sigma) ** 2) / np.sqrt(2.0 * np.pi * sigma**2)
+    flat = 1.0 / (max - min)
+    return Ns * normal + Nb * flat
+
+
+if __name__=="__main__": # -----------------------------
+
+  # generate a histogram data sample
+  SplusB_data = generate_data(N, min, max, pos, width, s)
+  # show the histogram
+  # bc, be, _ = plt.hist(SplusB_data, bins=35, rwidth=0.9)
+
+  # Create a histogram Container from the dataset
+  SplusB_histcontainer = HistContainer(n_bins=35, bin_range=(min, max), fill_data=SplusB_data)
+  SplusB_histcontainer.label = "artifical data"
+
+  # create the Fit object by specifying a density function
+  hist_PDFfit = Fit(data=SplusB_histcontainer, model_function=s_plus_bPDF)
+
+  # as an alternative, specify unnormalized fit function
+  hist_fit = Fit(data=SplusB_histcontainer, density=False,
+                 model_function=s_plus_b)
+  
+  hist_PDFfit.do_fit()  # 1st fit
+  hist_PDFfit.report()  # optional: print a report to the terminal
+
+  hist_fit.do_fit()  # 2nd fit
+  hist_fit.report()  # optional: print a report to the terminal
+
+  # Optional: create plot and show it
+  hist_plot = Plot([hist_PDFfit, hist_fit])
+  hist_plot.plot(asymmetric_parameter_errors=True)
+  plt.show()
diff --git a/examples/009_histogram_fit/README.rst b/examples/009_histogram_fit/README.rst
index 4b84ca33..5dbae86c 100644
--- a/examples/009_histogram_fit/README.rst
+++ b/examples/009_histogram_fit/README.rst
@@ -1,5 +1,6 @@
 * **01_histogram_fit.py**: How to fit a probability density function to binned one-dimensional data.
 * **02_absolute.py**: Same as the first example but the data is not normalized and the model function has an additional parameter for the scale.
+* **03_SplusB.py**: signal shape on top of a background distribution.
 
 Other relevant examples: